3DEM-Methods - User contributions [en]

2026Sun QwenCryoMarker

2026-09-03T05:57:51Z

WikiSysop: Created page with "== Citation == Sun, Y., Zhao, J., Xu, N., Wang, L., Ding, W. and Li, M. 2026. QwenCryoMarker: a universal post-processing framework for contamination-aware particle cleaning. Acta Crystallographica Sec. D. 82, 9 (2026). == Abstract == Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated by carbon edges, ice crystals, ethane bubbles and other high-contrast artifacts. These contaminants trigger abundant false positives in automated particle pickers..."

== Citation ==

Sun, Y., Zhao, J., Xu, N., Wang, L., Ding, W. and Li, M. 2026. QwenCryoMarker: a universal post-processing framework for contamination-aware particle cleaning. Acta Crystallographica Sec. D. 82, 9 (2026).

== Abstract ==

Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated
by carbon edges, ice crystals, ethane bubbles and other high-contrast artifacts.
These contaminants trigger abundant false positives in automated particle
pickers, severely hampering downstream 3D reconstruction. Existing methods
either avoid contamination implicitly (requiring dataset-specific tuning) or rely
on rule-based filters that fail on complex contamination patterns. Here, we
present QwenCryoMarker, a universal post-processing framework that converts
the outputs of arbitrary particle pickers into clean, high-precision particle sets.
Our pipeline consists of two core stages: (i) a visual large model (Qwen-Image-
Edit-2511) fine-tuned via supervised learning to generate pixel-accurate binary
contamination masks from raw micrographs and (ii) a lightweight contaminationaware
filtering module that discards particles when the contamination proportion
within their surrounding circular region exceeds a predefined threshold
ratio. Contamination is defined as all micrograph regions unsuitable for reliable
particle picking and subsequent 3D reconstruction, including carbon edges, ice
crystals, ethane bubbles, miscellaneous debris and dense protein aggregates.
The framework features plug-and-play deployment: it requires no per-dataset
parameter tuning or extra retraining, and maintains compatibility with classical
pickers (blob detection, template matching) as well as deep learning-based
pickers (Topaz, crYOLO etc.). We validate QwenCryoMarker on five diverse
CryoPPP benchmark datasets across four representative particle pickers.
Quantitatively, our method consistently boosts precision with an average
absolute gain of 0.009 and lifts the F1-score, while recall only drops slightly by an
average of 0.008. Segmentation benchmarking shows our model reaches a mean
intersection over union (IoU) of 0.629, surpassing that of the state-of-the-art
MicrographCleaner (0.551) by 14.2%. We further compare against multiple
segmentation baselines: U-Net (0.448), DeepLabV3+ (0.488), SAM (0.475),
ASOCEM (0.195) and IceBreaker (0.433). A downstream reconstruction case
study on EMPIAR-10017 verifies that particles filtered by QwenCryoMarker
yield cleaner 2D class averages and higher resolution 3D density maps (3.88
versus 3.97 A ˚ ). Qualitative visualization also confirms that QwenCryoMarker
stably eliminates false particles located on carbon films and ice crystals, independent
of the upstream particle-picking algorithm. By encapsulating contamination
suppression as a universal, model-agnostic post-processing module,
QwenCryoMarker offers a practical, robust, easy-to-deploy toolkit that greatly
improves particle-set quality without modifying existing cryo-EM workflows.
The framework is fully open-source and can be seamlessly integrated into
mainstream cryo-EM processing pipelines.

== Keywords ==

== Links ==

https://journals.iucr.org/paper?wan5006

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-09-03T05:56:45Z

WikiSysop: /* Automatic particle picking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

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Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

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== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

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== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

| Paper
| [[2026Sun_QwenCryoMarker]]
| QwenCryoMarker: Identification of contamination in micrographs
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

| Paper
| [[2025Schafer_CryoSift]]
| CryoSift: automatic selection of 2D classes
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

| Paper
| [[2026Kreymer_EM]]
| Expectation-Maximization reconstruction directly from micrographs
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Shugaeva_Prior]]
| Atomic modelling exploiting multiple AlphaFold2 predicted conformations
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Jain_ProtAcid]]
| CryoZETA and ComplexModeler for modelling protein and nucleic acids in cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2026Kreymer EM

2026-08-31T04:37:26Z

WikiSysop: Created page with "== Citation == Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110. == Abstract == A single-particle cryo-electron microscopy (cryo- EM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional (3-D) molecular structure at unknown locations, taken under unknown viewing directions. A..."

== Citation ==

Kreymer, S., Singer, A. and Bendory, T. 2026. Expectation-maximization for structure determination directly from cryo-em micrographs. Inverse problems and imaging. 27, (2026), 110.

== Abstract ==

A single-particle cryo-electron microscopy (cryo-
EM) measurement, called a micrograph, consists of multiple
two-dimensional tomographic projections of a three-dimensional
(3-D) molecular structure at unknown locations, taken under
unknown viewing directions. All existing cryo-EM algorithmic
pipelines first locate and extract the projection images, and then
reconstruct the structure from the extracted images. However, if
the molecular structure is small, the signal-to-noise ratio (SNR)
of the data is very low, making it challenging to accurately
detect projection images within the micrograph. Consequently,
all standard techniques fail in low-SNR regimes. To recover
molecular structures from measurements of low SNR, and in
particular small molecular structures, we devise an approximate
expectation-maximization algorithm to estimate the 3-D structure
directly from the micrograph, bypassing the need to locate the
projection images. We corroborate our computational scheme
with numerical experiments and present successful structure
recoveries from simulated noisy measurements.

== Keywords ==

== Links ==

https://www.aimsciences.org//article/doi/10.3934/ipi.2026033

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-31T04:36:40Z

WikiSysop: /* 3D Reconstruction */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

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Alternatively, you may write a mail to sympa6@cnb.csic.es with the subject

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or write sympa6@cnb.csic.es with the subject

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and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

| Paper
| [[2025Schafer_CryoSift]]
| CryoSift: automatic selection of 2D classes
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

| Paper
| [[2026Kreymer_EM]]
| Expectation-Maximization reconstruction directly from micrographs
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Shugaeva_Prior]]
| Atomic modelling exploiting multiple AlphaFold2 predicted conformations
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Jain_ProtAcid]]
| CryoZETA and ComplexModeler for modelling protein and nucleic acids in cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2026Jain ProtAcid

2026-08-24T05:12:03Z

WikiSysop: Created page with "== Citation == Jain, A., Cao, K. and Kihara, D. 2026. Computational approaches for protein–DNA/RNA complex modeling for Cryo-EM maps. Current Protocols. 6, 8 (2026), e70409. == Abstract == Cryogenic electron microscopy (cryo-EM) has become a key method in structural biology for determining macromolecular structures. Numerous computational tools have been developed to build atomic models from cryo-EM density maps. However, relatively few tools are available for model..."

== Citation ==

Jain, A., Cao, K. and Kihara, D. 2026. Computational approaches for protein–DNA/RNA complex modeling for Cryo-EM maps. Current Protocols. 6, 8 (2026), e70409.

== Abstract ==

Cryogenic electron microscopy (cryo-EM) has become a key method in structural
biology for determining macromolecular structures. Numerous computational
tools have been developed to build atomic models from cryo-EM density
maps. However, relatively few tools are available for modeling protein–
nucleic acid complexes. Here, we describe how to use two such methods developed
by our group, ComplexModeler and CryoZeta, with a focus on modeling
protein–nucleic acid complexes. Both tools are available through the EMSuite
web server, a freely accessible platform that hosts multiple methods for
cryo-EM structure modeling and validation. ComplexModeler integrates Diff-
Modeler and CryoREAD to construct protein–DNA/RNA complex structures
at resolutions of up to 5 Å. DiffModeler employs a diffusion model for backbone
tracing, followed by fitting AlphaFold2-predicted protein structures into
the traced backbone. CryoREAD identifies nucleotide components (phosphate,
sugar, and base), constructs the backbone, assigns sequences, and builds full
atomic models of DNA/RNA chains. CryoZeta uses a diffusion-based generative
model that integrates sequence-based structure prediction with cryo-EM
density features to generate accuratemodels of proteins, nucleic acids, and their
complexes. This article describes how to use these two tools on the EMSuite
web server through two modeling examples.

== Keywords ==

== Links ==

https://currentprotocols.onlinelibrary.wiley.com/doi/full/10.1002/cpz1.70409

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-24T05:11:10Z

WikiSysop: /* Fitting and docking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
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== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
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== How to subscribe to this page ==

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== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

| Paper
| [[2025Schafer_CryoSift]]
| CryoSift: automatic selection of 2D classes
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Shugaeva_Prior]]
| Atomic modelling exploiting multiple AlphaFold2 predicted conformations
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Jain_ProtAcid]]
| CryoZETA and ComplexModeler for modelling protein and nucleic acids in cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2025Schafer CryoSift

2026-08-21T05:37:36Z

WikiSysop: Created page with "== Citation == Schäfer, J.-H., Calza, A., Hom, K., Damodar, P., Peng, R., Bogdanović, N., Lander, G.C., Stagg, S.M. and Cianfrocco, M.A. 2025. CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection. Acta Crystallographica Sec. F. 81, 12 (2025), 517–526. == Abstract == Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge..."

== Citation ==

Schäfer, J.-H., Calza, A., Hom, K., Damodar, P., Peng, R., Bogdanović, N., Lander, G.C., Stagg, S.M. and Cianfrocco, M.A. 2025. CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection. Acta Crystallographica Sec. F. 81, 12 (2025), 517–526.

== Abstract ==

Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge in cryo-EM data-set processing. Here, we present a platformindependent convolutional neural network (CNN) tool for assessing the quality of 2D averages to enable the automatic selection of suitable particles for highresolution reconstructions, termed CryoSift. We integrate CryoSift into a fully automated processing pipeline using the existing cryosparc-tools library. Our integrated and customizable 2D assessment workflow enables high-throughput processing that accommodates experienced to novice cryo-EM users.

== Keywords ==

== Links ==

https://journals.iucr.org/f/issues/2025/12/00/ih5009/index.html

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-21T05:36:37Z

WikiSysop: /* 2D Classification and clustering */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

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Alternatively, you may write a mail to sympa6@cnb.csic.es with the subject

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To unsubscribe visit the page

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or write sympa6@cnb.csic.es with the subject

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and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

| Paper
| [[2025Schafer_CryoSift]]
| CryoSift: automatic selection of 2D classes
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Shugaeva_Prior]]
| Atomic modelling exploiting multiple AlphaFold2 predicted conformations
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2025Shugaeva Prior

2026-08-21T05:29:40Z

WikiSysop: Created page with "== Citation == Shugaeva, T., Howard, R.J., Haloi, N. and Lindahl, E. 2025. Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations. Communications Chemistry. 8, 1 (2025), 317. == Abstract == Modeling atomic coordinates into a target cryo-electron microscopy map is a crucial step in structure determination. Despite recent advances, proteins with multiple functional states remain a challenge - particularly when suita..."

== Citation ==

Shugaeva, T., Howard, R.J., Haloi, N. and Lindahl, E. 2025. Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations. Communications Chemistry. 8, 1 (2025), 317.

== Abstract ==

Modeling atomic coordinates into a target cryo-electron microscopy map is a crucial step in structure determination. Despite recent advances, proteins with multiple functional states remain a challenge - particularly when suitable molecular templates are unavailable for certain states, and the map resolution is not high enough to build de novo models. This is a common scenario, for example, among pharmacologically relevant membrane-bound receptors and transporters. Here, we introduce a refinement approach in which (i) several initial models are generated by stochastic subsampling of the multiple sequence alignment (MSA) space in AlphaFold2, (ii) the resulting models are subjected to structure-based k-means clustering, iii) density-guided molecular dynamics simulations are performed from the cluster representatives, and (iv) a final model is selected on the basis of both map fit and model quality. This results in improved fitting accuracy compared to single starting point scenarios for three membrane proteins (the calcitonin receptor-like receptor, L-type amino acid transporter and alanine-serine-cysteine transporter) which undergo substantial conformational transitions between functional states. Our results indicate that ensemble construction using generative AI combined with simulation-based refinement facilitates building of alternative states in several families of membrane proteins.

== Keywords ==

== Links ==

https://www.nature.com/articles/s42004-025-01751-4

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-21T05:29:00Z

WikiSysop: /* Fitting and docking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

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== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Shugaeva_Prior]]
| Atomic modelling exploiting multiple AlphaFold2 predicted conformations
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2025Zhang Struct2MapGAN

2026-08-21T05:21:23Z

WikiSysop: Created page with "== Citation == Zhang, C., Condon, A. and Dao Duc, K. 2025. Struc2mapGAN: improving synthetic cryogenic electron microscopy density maps with generative adversarial networks. Bioinformatics advances. 5, 1 (2025), vbaf179. == Abstract == Motivation Generating synthetic cryogenic electron microscopy 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex featu..."

== Citation ==

Zhang, C., Condon, A. and Dao Duc, K. 2025. Struc2mapGAN: improving synthetic cryogenic electron microscopy density maps with generative adversarial networks. Bioinformatics advances. 5, 1 (2025), vbaf179.

== Abstract ==

Motivation
Generating synthetic cryogenic electron microscopy 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex features present in experimental maps, such as secondary structure elements. As an alternative, we propose struc2mapGAN.

Results
Struc2mapGAN is a novel data-driven method that employs a generative adversarial network to produce improved experimental-like density maps from molecular structures. More specifically, struc2mapGAN uses a nested U-Net architecture as the generator, with an additional L1 loss term and further processing of raw training experimental maps to enhance learning efficiency. While struc2mapGAN can promptly generate maps after training, we demonstrate that it outperforms existing simulation-based methods for a wide array of tested maps and across various evaluation metrics.

Availability and implementation
The struc2mapGAN is publicly accessible via https://github.com/chenwei-zhang/struc2mapGAN.

== Keywords ==

== Links ==

https://academic.oup.com/bioinformaticsadvances/article/5/1/vbaf179/8221767

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-21T05:20:46Z

WikiSysop: /* Fitting and docking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

https://listas.csic.es/cnb/wws/subscribe/3demmethods

Alternatively, you may write a mail to sympa6@cnb.csic.es with the subject

<code>subscribe 3demmethods YourName YourFamilyName</code>

and an empty mail body.

To unsubscribe visit the page

https://listas.csic.es/cnb/wws/sigrequest/3demmethods

or write sympa6@cnb.csic.es with the subject

<code>unsubscribe 3demmethods</code>

and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Struct2MapGAN]]
| Struct2MapGAN: Simulation of cryoEM maps from atomic structures
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2026Wankowicz Atomic

2026-08-21T05:11:17Z

WikiSysop: Created page with "== Citation == Wankowicz, S.A. and Bonomi, M. 2026. From possibility to precision in macromolecular ensemble prediction. Nature Methods. (2026), 1–9. == Abstract == Proteins and other macromolecules exist as dynamic ensembles of interconverting conformations essential for catalysis, allosteric regulation and molecular recognition. While AI tools like AlphaFold have revolutionized static structure prediction, they cannot yet capture conformational ensembles. Progress..."

== Citation ==

Wankowicz, S.A. and Bonomi, M. 2026. From possibility to precision in macromolecular ensemble prediction. Nature Methods. (2026), 1–9.

== Abstract ==

Proteins and other macromolecules exist as dynamic ensembles of interconverting conformations essential for catalysis, allosteric regulation and molecular recognition. While AI tools like AlphaFold have revolutionized static structure prediction, they cannot yet capture conformational ensembles. Progress toward the next-generation ensemble predictors is limited by the lack of accurate, high-resolution ground-truth data at the scale required for training and validation—no single experimental technique fully resolves the atomistic complexity of conformational landscapes, and challenges remain in defining, representing, comparing and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and leveraging these to build benchmarks and ensemble-specific validation protocols. We also discuss how ensemble prediction will drive an interactive cycle of experimental and computational innovation, ultimately moving structural biology beyond static snapshots toward a dynamic understanding of the full complexity of molecular behavior.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41592-026-03084-z

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-21T05:10:32Z

WikiSysop: /* Books and reviews */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

https://listas.csic.es/cnb/wws/subscribe/3demmethods

Alternatively, you may write a mail to sympa6@cnb.csic.es with the subject

<code>subscribe 3demmethods YourName YourFamilyName</code>

and an empty mail body.

To unsubscribe visit the page

https://listas.csic.es/cnb/wws/sigrequest/3demmethods

or write sympa6@cnb.csic.es with the subject

<code>unsubscribe 3demmethods</code>

and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Wankowicz_Atomic]]
| Review on ensemble modelling
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2022Dickerson InelasticContribution

2026-08-20T08:53:49Z

WikiSysop: Created page with "== Citation == Dickerson, J.L. and Russo, C.J. 2022. Phase contrast imaging with inelastically scattered electrons from any layer of a thick specimen. Ultramicroscopy. 237, (2022), 113511. == Abstract == A controversy exists as to whether the signal in a high resolution phase contrast electron micrograph of a particle in a thick specimen is the same irrespective of the particle’s position along the beam axis. Different conceptions of inelastic scattering and its eff..."

== Citation ==

Dickerson, J.L. and Russo, C.J. 2022. Phase contrast imaging with inelastically scattered electrons from any layer of a thick specimen. Ultramicroscopy. 237, (2022), 113511.

== Abstract ==

A controversy exists as to whether the signal in a high resolution phase contrast electron micrograph of a particle in a thick specimen is the same irrespective of the particle’s position along the beam axis. Different conceptions of inelastic scattering and its effects on wave interference have led to radically different expectations about the degree of phase contrast vs. depth. Here we examine the information available from bright field phase contrast images of small crystalline particles on the top or bottom of a thick support. The support is an aluminium foil which has strong plasmon resonances that cause a large proportion of the electron beam to lose energy in transit. Phase contrast micrographs of the atomic lattice of two ensembles of platinum particles were measured in an energy loss window corresponding to the first plasmon resonance. The signal measured for particles on top was equal to that for particles on the bottom of the foil to within a 99% confidence interval, and the measurements exclude other models of depth dependent phase contrast in the literature to
. These observations are consistent with quantum theory which considers dynamical effects as independent of event sequence and is distinct from the “top-bottom effect” observed in amplitude contrast. We thus confirm that phase contrast using inelastically scattered electrons can be obtained equally well from particles within any layer of a thick specimen.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S0304399122000468

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-20T08:40:04Z

WikiSysop: /* Image formation */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

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Alternatively, you may write a mail to sympa6@cnb.csic.es with the subject

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To unsubscribe visit the page

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or write sympa6@cnb.csic.es with the subject

<code>unsubscribe 3demmethods</code>

and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_InelasticContribution]]
| Phase contributions of inelastically scattered electrons
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2026Fadini Rocket

2026-08-20T05:12:11Z

WikiSysop: Created page with "== Citation == Fadini, A., Li, M., McCoy, A.J., Banjara, S., Okumura, H., Napier, E., Fontana, P., Khan, A.R., Jovine, L., Terwilliger, T.C. and others 2026. AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network. Nature methods. 23, (2026), 785–795. == Abstract == Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, key cha..."

== Citation ==

Fadini, A., Li, M., McCoy, A.J., Banjara, S., Okumura, H., Napier, E., Fontana, P., Khan, A.R., Jovine, L., Terwilliger, T.C. and others 2026. AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network. Nature methods. 23, (2026), 785–795.

== Abstract ==

Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, key challenges persist in modeling side-chain packing, condition-dependent conformational changes and biomolecular interactions, largely because of limited high-quality training data. At the same time, emerging experimental techniques such as cryo-electron microscopy (cryo-EM), cryo-electron tomography (cryo-ET) and high-throughput crystallography are generating vast amounts of structural information but converting these data into mechanistically interpretable atomic models often remains difficult. Here we show that integrating experimental measurements directly into protein structure prediction can overcome these limitations. We introduce ROCKET, an augmentation of AlphaFold2 that refines predicted structures using cryo-EM, cryo-ET and X-ray crystallography data. By optimizing structures in the space of coevolutionary embeddings rather than Cartesian coordinates, ROCKET captures biologically meaningful structural variation that is inaccessible to AlphaFold2 alone and to existing automated modeling approaches, especially when the signal-to-noise ratio is low. ROCKET enables scalable, automated model building without retraining and provides a general framework for integrating experimental observables with biomolecular machine learning.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41592-026-03047-4

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-20T05:11:19Z

WikiSysop: /* Fitting and docking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
* You will be sure that your paper has more chances of being cited.
* You can add whatever information you find relevant to your method, external links, links to other papers, etc.
* You can link your method to a web page providing the corresponding software, so you know people can use your algorithms.
* You can use the Wiki search capabilities to locate relevant articles even if they are not at the topic you are looking at.
* As a community we will "reconstruct" our history.
* You can select the "watch" option above to receive notifications in case of further changes in the Main Page.

Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

* Add your articles at the right place (you can add a single article in several topics if it is relevant to all of them). The easiest way to add an article is by editing the corresponding topic in the main page, adding your entry, then save the main page. Click on the red link that appears on your new entry, copy and paste the Wiki following code, add the information of your article

== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

== Related methods ==

== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
* Be respectful to other people's work.

== How to subscribe to this page ==

You may subscribe to this page so that you will be notified every time someone adds a new method. To do so, you have to register in the page, and edit your preferences ("my preferences"). Be sure that you have activated the option "E-mail me when a page on my watchlist is changed". Finally, click on the "Watch" tab at the top of this page.

You may subscribe to changes on this page on the mailing list 3demmethods@cnb.csic.es. To send a new method to this mail list just send a mail to 3demmethods@cnb.csic.es

To subscribe to this list, visit the web page

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and an empty mail body.

== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Fadini_Rocket]]
| Rocket: AlphaFold as a prior to model cryoEM maps
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

== Editing useful links ==

* [http://www.mediawiki.org/wiki/Help:Formatting Text formatting in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Links Adding links in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Images Adding images in Mediawiki]
* [http://www.mediawiki.org/wiki/Help:Tables Making tables in Mediawiki]

2025Gyawali Multimodal

2026-08-13T06:37:40Z

WikiSysop: Created page with "== Citation == Gyawali, R., Dhakal, A. and Cheng, J. 2025. Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination. Communications Chemistry. 8, 1 (2025), 320. == Abstract == Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a cr..."

== Citation ==

Gyawali, R., Dhakal, A. and Cheng, J. 2025. Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination. Communications Chemistry. 8, 1 (2025), 320.

== Abstract ==

Cryo-electron microscopy (cryo-EM) is a key technology for determining the structures of proteins, particularly large protein complexes. However, automatically building high-accuracy protein structures from cryo-EM density maps remains a crucial challenge. In this work, we introduce MICA, a fully automatic and multimodal deep learning approach combining cryo-EM density maps with AlphaFold3-predicted structures at both input and output levels to improve cryo-EM protein structure modeling. It first uses a multi-task encoder-decoder architecture with a feature pyramid network to predict backbone atoms, Cα atoms, and amino acid types from both cryo-EM maps and AlphaFold3-predicted structures, which are used to build an initial backbone model. This model is further refined using AlphaFold3-predicted structures and density maps to build final atomic structures. MICA significantly outperforms other state-of-the-art deep learning methods in terms of both modeling accuracy and completeness, and is robust to protein size and map resolution. Additionally, it builds high-accuracy structural models with an average template-based modeling score (TM-score) of 0.93 from recently released high-resolution cryo-EM density maps, showing it can be used for real-world, automated, accurate protein structure determination.

== Keywords ==

== Links ==

https://www.nature.com/articles/s42004-025-01718-5

== Related software ==

== Related methods ==

== Comments ==

Main Page

2026-08-13T06:37:09Z

WikiSysop: /* Fitting and docking */

== Presentation ==

This web page is intended to be an extensive repository of three-dimensional electron microscopy (3DEM) methods. The advantages of having such a repository are

* You know which are the relevant articles in a particular topic, therefore, paper introductions, reviews, etc. are easier to write and you make sure you will cite all the related articles.
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Developing image processing methods for 3DEM is not "[[well-paid]]" in terms of citations and impact factor. However, it is crucial for the advance of the field. Gathering methodological papers in a portal will help to increase the recognition of the field.

Please:

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== Citation ==

== Abstract ==

== Keywords ==

== Links ==

== Related software ==

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== Comments ==

* Maintain the information of each article as complete as possible.
* Sort articles by years and authors.
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== How to subscribe to this page ==

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== Electron microscopy images ==

=== Useful resources ===

[https://www.google.com/maps/d/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&usp=sharing Map of cryoEM microscopes and labs in the world]

[https://www.ebi.ac.uk/emdb/genealogy CryoEM genealogy]

[https://rico.ibs.fr/helixplorer/resolution/ Resolution game]

[https://rico.ibs.fr/helixplorer/fourier Fourier transform game]

=== Online courses and Learning material ===

[http://cryo-em-course.caltech.edu Caltech] [https://www.coursera.org/learn/cryo-em (same course in Coursera)] [https://em-learning.com (latest version of the course in EM-learning)]

[ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/EM-course MRC]

[https://www2.mrc-lmb.cam.ac.uk/research/scientific-training MRC training channel]

[http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/MadridJan2014EM CNB-CSIC]

[https://www.youtube.com/watch?v=XdKO1iaPP8o Workshop on Computational methods for CryoEM]

[https://www.youtube.com/watch?v=as4seOPszL0 Workshop on Management of large CryoEM facilities]

[http://www.ccpem.ac.uk/training/icknield_2017/icknield_2017.php Icknield Course on Model Building and Refinement for High Resolution EM Maps]

[https://www.jove.com/video/51087/optimized-negative-staining-high-throughput-protocol-for-examining Tutorial on how to prepare negative staining samples]

[https://www.youtube.com/playlist?list=PL8_xPU5epJdfd5fM2CjQItR-iRlIEIJk8 Tutorial on how to prepare samples]

[https://www.jove.com/video/52311/do-s-don-ts-cryo-electron-microscopy-primer-on-sample-preparation Do's and don'ts on sample preparation]

[http://nramm.nysbc.org/2017-workshop-lectures NRAMM Workshop 2017] [https://nccat.nysbc.org/activities/courses/nccat-spa-short-course-2022 (course slides)]

[https://www.youtube.com/user/SBGridTV/videos SBGrid videos about the programs they offer]

[http://i2pc.es/coss/Docencia/ImageProcessing/imageProcessingInEM.pdf Madrid course on single particle analysis]

[https://www.youtube.com/watch?v=F1yXJaZ2H5o&list=PLFEB3YHuxu11I4Qgj6K5jmWcghsFnE09Q CCP-EM Spring symposium 2019]

[https://www.youtube.com/playlist?list=PLFEB3YHuxu11Jp_pOCIEtXxSqozFHve0O CCP-EM Spring symposium 2020]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZel2mj6S4FPjWwsvj2LPqLYL NCCAT Single Particle short course 2020]

[https://www.cellstructureatlas.org Cell atlas book by Grant Jensen and Catherine Oikonomou]

[https://va.tech.purdue.edu/cryoVR/index.php Purdue CryoEM Virtual Reality Augmented Training]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZek9D0SZk0OVtTeFVETa1sPG NCCAT Short course on Tomography]

[https://www.google.com/maps/d/u/0/viewer?mid=1eQ1r8BiDYfaK7D1S9EeFJEgkLggMyoaT&ll=-3.81666561775622e-14%2C-37.83892653579875&z=1 Map with CryoEM Facilities]

[https://www.youtube.com/playlist?list=PLhiuGaXlZZeliWnyp_wtRqPr_QkX00um7 NCCAT Single Particle Analysis short course]

[https://algosb2023.loria.fr/lectures/ Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese]

[https://cryoem.world/ One world CryoEM technical talks]

[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]

[https://www.diamond.ac.uk/Instruments/Biological-Cryo-Imaging/eBIC/Training/Courses-and-workshops/2025-EventsCW/eBIC-in-situ-Cryo-ET-Workshop-2025.html In Situ CryoET eBic]

=== Image formation ===

{|

| Paper
| [[1971Erickson_CTF]]
| CTF model
|-

| Paper
| [[1971Glaeser_Damage]]
| Radiation damage
|-

| Chapter
| [[1971Hanszen_CTF]]
| Image formation model
|-

| Paper
| [[1971Thon_Model]]
| CTF model
|-

| Paper
| [[1974Taylor_Diffraction]]
| Electron diffraction of crystals
|-

| Paper
| [[1975Unwin_Imaging]]
| Radiation dose
|-

| Paper
| [[1977Wade_Model]]
| CTF model
|-

| Paper
| [[1978Wade_Model]]
| CTF model
|-

| Paper
| [[1979Hayward_Radiation]]
| Radiation damage
|-

| Paper
| [[1984Cohen_Validity]]
| Validity of the CTF model at high frequencies
|-

| Paper
| [[1988Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[1992Wade_Model]]
| CTF model
|-

| Paper
| [[1993Toyoshima_Model]]
| Amplitud constrast
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2004Egerton_Damage]]
| Radiation damage
|-

| Paper
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
|-

| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2009Baxter_NoiseCharacterization]]
| Characterization of the different noise sources in cryo-EM
|-

| Book
| [[2009Rose_Optics]]
| Geometrical Charged-Particle Optics
|-

| Paper
| [[2010Baker_Damage]]
| Radiation damage dependence on resolution
|-

| Paper
| [[2010Bammes_Damage]]
| Radiation damage dependence on temperature
|-

| Paper
| [[2010Gomez_Multislice]]
| Simulation of the multi slice model
|-

| Paper
| [[2010Zewail_FourDimensional]]
| Review on the use of ultrafast EM
|-

| Paper
| [[2011Bammes_CCD]]
| Performance of CCD cameras
|-

| Paper
| [[2011Glaeser_Coma]]
| Image formation model including coma
|-

| Paper
| [[2011Milazzo_DirectDetectors]]
| Evaluation of Direct Detectors
|-

| Paper
| [[2011Rullgard_ImageSimulation]]
| Accurate simulation of EM images
|-

| Paper
| [[2011Zhang LimitingFactors]]
| Limiting factor for atomic resolution in EM
|-

| Paper
| [[2012Bammes_DirectDetection]]
| Performance of Direct detectors
|-

| Paper
| [[2012Campbell_MotionCorrection]]
| Beam induced motion correction and direct detectors
|-

| Paper
| [[2012Shang_HydrationLayer]]
| Simulation of PDB volumes explicitly considering the hydration layer
|-

| Paper
| [[2013Egerton_RadiationDamage]]
| Review of TEM radiation damage and experimental ways of reducing it
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li X K2 noisemodels]]
| Influence of electron dose rate on electron counting images recorded with the K2 camera
|-

| Paper
| [[2013Ruskin_DQE]]
| Measurement of the DQE, camera MTF, and the noise power spectrum of cameras
|-

| Paper
| [[2013Shigematsu H_Noisemodels]]
| Noise models and cryo-EM drift correction with a direct-electron camera
|-

| Paper
| [[2013Vulovic_CTFApproximations]]
| When to use the different approximations performed so that a projection with linear CTF is valid
|-

| Thesis
| [[2013Vulovic_ImageFormation]]
| Ph.D. Thesis on the image formation in cryo-EM
|-

| Paper
| [[2014Danev_PhasePlate]]
| Volta potential phase plate
|-

| Paper
| [[2015Chiu_K2]]
| Characterization of K2
|-

| Paper
| [[2015Hawkes_AberrationCorrection]]
| Historical account of the development of lens corrections
|-

| Paper
| [[2015Koeck_Quadratic]]
| Limitations of the linear approximation and use of the quadratic terms
|-

| Paper
| [[2015Kuijper_FEI]]
| Description of the FEI Falcons
|-

| Paper
| [[2015Lobato_MULTEM]]
| Simulation of multislice diffraction
|-

| Paper
| [[2015McMullan_AmorphousIce]]
| Beam induced movement is caused by Brownian motion
|-

| Paper
| [[2016Glaeser_Behaviour]]
| Behaviour of the specimen under the electron beam
|-

| Paper
| [[2016Koeck_ADF]]
| Simulations of Annular Dark Field TEM
|-

| Paper
| [[2017Fan_VPP]]
| 3D Reconstruction with under-focus and over-focus Volta Phase Plate micrographs
|-

| Paper
| [[2017Koeck_ApertureDesign]]
| Aperture design for singe side band imaging
|-

| Paper
| [[2017Mishyna_DNARadiation]]
| Review of radiation damage on DNA
|-

| Paper
| [[2018Anoshina_Simulation]]
| Simulation of 2D and 3D EM images
|-

| Paper
| [[2018Downing_DepthOfField]]
| Effects of the Depth of Field
|-

| Paper
| [[2018DeJonge_SpatialResolution]]
| Theory of spatial resolution in liquid water or ice layers
|-

| Paper
| [[2018Faruqi_DED]]
| Review of Direct Detectors
|-

| Paper
| [[2018Hattne_RadiationDamage]]
| Analysis of radiation damage in EM
|-

| Paper
| [[2018Hettler_Charging]]
| Charging of carbon thin films
|-

| Paper
| [[2018Koeck_PhaseShift]]
| Design of a phase shift device
|-

| Paper
| [[2018Noble_ParticleDistribution]]
| Particle distribution and ice thickness for Single Particles
|-

| Paper
| [[2018Russo_ChargeAccumulation]]
| Charge accumulation in electron cryomicroscopy
|-

| Paper
| [[2018Russo_SingleBandEM]]
| Ewald sphere correcion using single-side band image processing
|-

| Paper
| [[2019Peet_EnergyDependence]]
| Energy dependence of radiation damage
|-

| Paper
| [[2020Bromberg_Aberrations]]
| Estimation of strong high-order aberrations
|-

| Paper
| [[2020Gruza_Atomic]]
| Detailed atomic models considering local charges and directional bonds
|-

| Paper
| [[2020Naydenova_Buckling]]
| Beam induced movement explained as ice buckling
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2020Tichelaar_Thick]]
| Effect of sample thickness on the CTF
|-

| Paper
| [[2020Yip_Atomic]]
| Atomic resolution by monochromator and a second-generation spherical aberration corrector
|-

| Paper
| [[2020Zhang_LimitsSimulated]]
| Simulation of micrographs and 3D reconstruction for low weight proteins (14kDa)
|-

| Paper
| [[2021Egerton_Inelastic]]
| PSF of inelastic scattering
|-

| Paper
| [[2021Himes_Simulation]]
| Simulation of TEM images with special attention to inelastic scattering
|-

| Paper
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
|-

| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-

| Paper
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
|-

| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-

| Paper
| [[2022Heymann_PSSNR]]
| Progressive Spectral Signal-to-Noise Ratio to assess quality and radiation damage
|-

| Paper
| [[2022Dickerson_Inelastic]]
| The role of inelastic scattering in thick specimens
|-

| Paper
| [[2022Kulik_TAAM]]
| Theoretical 3D electron diffraction electrostatic potential maps of proteins
|-

| Paper
| [[2022Ravikumar_SideChains]]
| Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography
|-

| Paper
| [[2023Bromberg_Complex]]
| CTF and Ewald sphere correction using complex-valued images
|-

| Paper
| [[2023Heymann_Ewald]]
| The Ewald sphere/focus gradient does not limit the resolution of cryoEM reconstructions
|-

| Paper
| [[2023McMullan_100kV]]
| CryoEM at 100kV
|-

| Paper
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
|-

| Paper
| [[2023Shi_Compression]]
| Protein compression due to ice formation
|-

| Paper
| [[2024Bochtler_Probes]]
| X-rays, electrons, and neutrons as probes of atomic matter
|-

| Paper
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
|-

| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-

| Paper
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
|-

| Paper
| [[2024Remis_Damage]]
| Radiation damage revealed by phase plates
|-

| Paper
| [[2025Dickerson_Damage]]
| Reduced radiation damage at liquid helium temperature
|-

| Paper
| [[2025Evans_LowDim]]
| SPA images are intrinsically low dimensional
|-

| Paper
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-

| Paper
| [[2026Heymann_Ewald]]
| The relationship between the Ewald sphere and exit wave explored using focal series electron micrographs
|-

| Paper
| [[2026Motta_Dublins]]
| Dublin lens for 100kV microscopes
|-

| Paper
| [[2026Obrien_CryoJax]]
| CryoJAX: Simulation of SPA images from atomic coordinates
|-

| Paper
| [[2026Petrov_Laser]]
| Laser phase plates
|-

| Paper
| [[2026Wazny_Dose]]
| Influence of total electron dose on the quality of nucleic acids potential maps in Cryo-EM
|-

|}

=== Collection geometry ===

{|

| Chapter
| [[1980Hoppe_Wedge]]
| Missing wedge
|-

| Paper
| [[1987Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1995Penczek_Dual]]
| Dual axis tomography
|-

| Paper
| [[1997Mastronarde_Dual]]
| Dual axis tomography
|-

| Paper
| [[2003Ludtke_FocusPairs]]
| Focus pairs for single particles
|-

| Paper
| [[2005Lanzavecchia_Conical]]
| Conical tomography
|-

| Paper
| [[2005Zampighi_Conical]]
| Conical tomography
|-

| Paper
| [[2006Leschziner_OT]]
| Orthogonal Tilt
|-

| Paper
| [[2006Messaoudi_Multiple]]
| Multiple axis tomography
|-

| Paper
| [[2012Kudryashev_FocusPairs]]
| Focus pairs tomography
|-

| Paper
| [[2014Hovden_TiltFocus]]
| Combining tilt series with focus series
|-

| Paper
| [[2015Sorzano_RandomConicalTilt]]
| General formulation of Random Conical Tilt
|-

| Paper
| [[2017Hagen_DoseTomography]]
| Dose optimization for subtomogram averaging
|-

| Paper
| [[2017Tan_PreferredViews]]
| Solving preferred views problems through tilting
|-

| Paper
| [[2017Donati_Compressed]]
| Compressed sensing for STEM
|-

| Paper
| [[2018Oveisi_Stereo]]
| Stereo-vision with EM
|-

| Paper
| [[2018Cheng_BeamShift]]
| Fast image acquisition through beam-shift
|-

| Paper
| [[2019Wu_BeamShiftAndTilt]]
| Fast image acquisition through beam-shift and beam tilt control
|-

| Paper
| [[2022Calcraft_SPATilts]]
| SPA+Tilted acquisitions
|-

| Paper
| [[2023Seifer_RevisedSaxton]]
| Revised Saxton geometry for tilt series acquisition
|-

|}

=== Sample preparation ===

{|

| Paper
| [[1982Dubochet_Sample]]
| Vitreous ice
|-

| Paper
| [[1986Lepault_Sample]]
| Fast freezing
|-

| Paper
| [[1995Dubochet_Sample]]
| High-pressure freezing
|-

| Paper
| [[1995VanMarle_Sample]]
| Sample damages in resin
|-

| Paper
| [[1998Adrian_Sample]]
| Cryo negative staining
|-

| Paper
| [[2002DeCarlo_Damage]]
| Radiation damage in cryonegative staining
|-

| Paper
| [[2002Hsieh_Sample]]
| Cryofixation
|-

| Paper
| [[2004AlAmoudi_Sample]]
| CEMOVIS
|-

| Paper
| [[2008Studer_Sample]]
| Review on high pressure freezing
|-

| Paper
| [[2009Pierson_Sample]]
| Review on sample preparation for electron tomography
|-

| Paper
| [[2010Zhang_OpNS]]
| Optimized negative staining (OpNS) for small protein and lipoprotein imaging
|-

| Paper
| [[2012Zhang_Cryo-PS]]
| Cryo-positive staining (Cryo-PS)
|-

| Paper
| [[2014Russo_GoldGrids]]
| Gold grids for single particles
|-

| Paper
| [[2015Cabra_Sample]]
| Review on sample preparation for single particles with videos
|-

| Paper
| [[2015Chari_ProteoPlex]]
| Fast evaluation of the structural stability
|-

| Paper
| [[2016Passmore_Review]]
| Tutorial chapter on sample preparation
|-

| Paper
| [[2016Razinkov_Vitrification]]
| New vitrification method
|-

| Paper
| [[2016Takizawa_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2016Thompson_Sample]]
| Review on sample preparation for EM
|-

| Paper
| [[2017Arnold_BlottingFree]]
| Blotting-free preparation
|-

| Paper
| [[2017Earl_review]]
| Review of sample preparation
|-

| Paper
| [[2017Feng_SprayingPlunging]]
| Spraying plunging
|-

| Paper
| [[2017He_FIB]]
| Cryo FIB lamella for TEM
|-

| Paper
| [[2017Peitsch_Sample]]
| iMEM: Isolation of Plasma Membrane for Cryoelectron Microscopy
|-

| Paper
| [[2017Scapin_Storage]]
| Cryo storage of samples
|-

| Paper
| [[2017Schaffer_FocusedIonBeam]]
| Focused Ion Beam sample preparation for membrane proteins
|-

| Paper
| [[2017Scherr_HydrogelNanomembranes]]
| Sample preparation for membrane proteins
|-

| Paper
| [[2018Anderson_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Arnold_Review]]
| Review on sample preparation with special emphasis on microfluidic approaches
|-

| Paper
| [[2018Ashtiani_femtolitre]]
| Delivery of femtolitre droplets using surface acoustic wave based atomisation for cryo-EM grid preparation
|-

| Paper
| [[2018Dandey_Spotiton]]
| Spotiton, a device for vitrification
|-

| Paper
| [[2018Gewering_Detergents]]
| Detergent background in negative stain
|-

| Paper
| [[2018Li_CLEM]]
| Correlated light and EM
|-

| Paper
| [[2018Noble_Reducing]]
| Reducing particle adsorption
|-

| Paper
| [[2018Palovcak_Graphene]]
| Preparation of graphene-oxide cryo-EM grids
|-

| Paper
| [[2018Rice_Ice]]
| Routine determination of ice thickness
|-

| Paper
| [[2018Schmidli_Miniaturized]]
| Protein isolation and sample preparation
|-

| Paper
| [[2018Wei_Grids]]
| "Self-wicking" nanowire grids
|-

| Paper
| [[2019DImprima_Denaturation]]
| Protein denaturation at the air-water interface and how to prevent it
|-

| Paper
| [[2019Rubinstein_ultrasonic]]
| Ultrasonic specimen preparation device
|-

| Paper
| [[2019Song_FalconIII]]
| Comparison of the modes of Falcon III
|-

| Paper
| [[2020Cianfrocco_Wrong]]
| What could go wrong?
|-

| Paper
| [[2020Egelman_Ice]]
| Problems with the ice
|-

| Paper
| [[2020Fassler_Printing]]
| 3D printed cell culture grid holder
|-

| Paper
| [[2020Klebl_Deposition]]
| Sample deposition onto CryoEM grids: sprays and jets
|-

| Paper
| [[2020Maeots_TimeResolved]]
| Time resolved CryoEM by microfluidics
|-

| Paper
| [[2020Tan_ThroughGrid]]
| Through-grid wicking enables high-speed 1 cryoEM specimen preparation
|-

| Paper
| [[2020Yoder_TimeResolved]]
| Time resolved CryoEM by light estimulation
|-

| Paper
| [[2020Zachs_FIB]]
| Automation for FIB milling
|-

| Paper
| [[2021Bieber_FIBET]]
| Sample preparation for correlative FIB milling and CryoET
|-

| Paper
| [[2021Budell_TimeResolved]]
| Time resolved CryoEM with Spotiton
|-

| Paper
| [[2021Casasanta_Microchip]]
| Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy
|-

| Paper
| [[2021Frechard_Preparation]]
| Optimization of Sample Preparation
|-

| Paper
| [[2021Engstrom_Nitrogen]]
| Samples vitrified in boiling nitrogen
|-

| Paper
| [[2021Jagota_GoldNanoparticles]]
| Gold nanoparticles to assess flexibility
|-

| Paper
| [[2021Jiang_MoAu]]
| Holey Gold Films on Molybdenum Grids
|-

| Paper
| [[2021Jonaid_Liquid]]
| Liquid phase EM
|-

| Paper
| [[2021Ki_Conformational]]
| Conformational Distribution of a Small Protein with Nanoparticle-Aided CryoEM
|-

| Paper
| [[2021Li_detergents]]
| The effect of detergents on preferential orientations
|-

| Paper
| [[2021Voss_Melting]]
| Rapid melting and revitrification as an approach to microsecond time-resolved cryoEM
|-

| Paper
| [[2021Zhang_Pegylation]]
| Improving particle quality in cryo-EM by PEGylation
|-

| Paper
| [[2022Chen_Detergents]]
| Role of detergents in the air-water interface
|-

| Paper
| [[2022Levitz_Chameleon]]
| Effects of dispense-to-plunge speed on particle concentration, complex formation, and final resolution
|-

| Paper
| [[2022Naydenova_Grid]]
| Integrated wafer-scale manufacturing of electron cryomicroscopy specimen supports
|-

| Paper
| [[2022Russo_Review]]
| Review of sample preparation issues
|-

| Paper
| [[2022Scher_FIB]]
| Sample preparation for FIB-SEM and Correlative microscopy
|-

| Paper
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids
|-

| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-

| Paper
| [[2023Han_Sample]]
| Challenges in making ideal cryo-EM samples
|-

| Paper
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
|-

| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-

| Paper
| [[2023Neselu_IceThickness]]
| Effect of ice thickness on resolution
|-

| Paper
| [[2023Torino_TimeResolved]]
| Device for the preparation of time-resolved CryoEM experiments
|-

| Paper
| [[2023Venien_Membrane]]
| Review on the preparation of membrane proteins
|-

| Paper
| [[2023Zheng_Ultraflat]]
| Uniform thin ice on ultraflat graphene grids
|-

| Paper
| [[2024Esfahani_SPOTRASTR]]
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-

| Paper
| [[2024Abe_LEA]]
| LEA proteins to reduce the air-water interface interaction
|-

| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-

| Paper
| [[2024Harley_40]]
| Pluge freezing over 40 degrees
|-

| Paper
| [[2024Henderikx_Vitrojet]]
| Use cases of Vitrojet
|-

| Paper
| [[2024Hsieh_MinIce]]
| Minimization of the ice contamination for cryoET
|-

| Paper
| [[2024Liu_Graphene]]
| Review of the use of graphene for grid preparation
|-

| Paper
| [[2024Mueller_Facility]]
| Sample workflow at the facility
|-

| Paper
| [[2024Tuijtel_Lamellae]]
| Optimizing lamellae for subtomogram averaging
|-

| Paper
| [[2024Yadav_Orientation]]
| Experimental factors affecting orientation distribution
|-

| Paper
| [[2025Alexadrescu_Mix]]
| ‘Mix-it-up’: accessible time-resolved cryo-EM on the millisecond timescale
|-

| Paper
| [[2025Chen_Detergent]]
| Review on the use of detergents to extract membran proteins and their effects on CryoEM
|-

| Paper
| [[2025Elad_Review]]
| Review of sample preparation for in situ protein visualization
|-

| Paper
| [[2025Grant_Nanodisc]]
| Review on the use of nanodiscs for sample preparation
|-

| Paper
| [[2025Gusach_Diffusion]]
| Sample vitrification faster than protein diffusion
|-

| Paper
| [[2025Haynes_OptimalIce]]
| Vitrification conditions for optimal ice thickness
|-

| Paper
| [[2025Sun_PlasmaMembranes]]
| Sample preparation pipeline for plasma membrane analysis by CryoET
|-

| Paper
| [[2026Eluru_MISO]]
| MISO: microfluidic protein isolation from a single cell colony
|-

| Paper
| [[2026Premaraj_DualJet]]
| Dual jet vitrification
|-

|}

=== Automated data collection ===

{|

| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-

| Paper
| [[1992Koster_Automatic]]
| Automated data collection
|-

| Paper
| [[1996Fung_Automatic]]
| Automated data collection for tomography
|-

| Paper
| [[2001Zhang_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2003Ziese_Automatic]]
| Automated autofocusing
|-

| Paper
| [[2004Potter_Automatic]]
| Automated sample loading
|-

| Paper
| [[2004Zheng_Automatic]]
| Automated data collection
|-

| Paper
| [[2005Lei_Automatic]]
| Automated data collection: AutoEM
|-

| Paper
| [[2005Suloway_Automatic]]
| Automated data collection: Leginon
|-

| Paper
| [[2007Yoshioka_RCT]]
| Automated Random Conical Tilt
|-

| Paper
| [[2011Korinek_TOM2]]
| Automated acquisition with TOM2
|-

| Paper
| [[2015Li_UCSFImage]]
| Automated acquisition with UCSFImage
|-

| Paper
| [[2016Gil_Fuzzy]]
| Real time decisions during acquisition with neuro-fuzzy method
|-

| Paper
| [[2016Liu_TiltControl]]
| Accurate control of the tilt angle for electron tomography
|-

| Paper
| [[2016Vargas_FoilHole]]
| Determination of image quality at low magnification
|-

| Paper
| [[2017Alewijnse_Best]]
| Best practices for managing large CryoEM facilities
|-

| Paper
| [[2017Biyani_Focus]]
| Automatic processing of micrographs
|-

| Paper
| [[2018Gomez_Facilities]]
| Use of Scipion at facilities
|-

| Paper
| [[2018Sorzano_Gain]]
| Estimation of the DDD camera gain or residual gain
|-

| Paper
| [[2019Chreifi_TiltSeries]]
| Rapid tilt-series acquisition for electron cryotomography
|-

| Paper
| [[2019Eng_ImageCompression]]
| 3D Reconstruction from compressed images
|-

| Paper
| [[2019Eisenstein_FISE]]
| Improved applicability and robustness of fast cryo-electron tomography data acquisition
|-

| Paper
| [[2019Hamaguchi_CryoARM]]
| CryoARM data acquisition
|-

| Paper
| [[2019Maluenda_Scipion]]
| Automated workflow processing for facilities
|-

| Paper
| [[2019Schorb_ET]]
| Automated acquisition in Electron Tomography
|-

| Paper
| [[2019Tegunov_Warp]]
| Automatic micrograph processing with Warp
|-

| Paper
| [[2019Thompson_Protocol]]
| Protocol for EM acquisition
|-

| Paper
| [[2020Baxa_Facility]]
| Operational workflow in a facility
|-

| Paper
| [[2020Guo_EER]]
| Electron event representation for acquisition
|-

| Paper
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
|-

| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-

| Paper
| [[2020Sader_Facility]]
| Microscope installation and operation in a facility
|-

| Paper
| [[2020Schenk_CryoFlare]]
| CryoFlare, automatic data acquisition
|-

| Paper
| [[2020Stabrin_Transphire]]
| TranSPHIRE: Automated and feedback-optimized on-the-fly processing for cryo-EM
|-

| Paper
| [[2020Yokoyama_Good]]
| Deep learning for determining good regions in a grid
|-

| Paper
| [[2020Weis_Acquisition]]
| Suggestions for high-quality and high-throughput acquisition
|-

| Paper
| [[2021Feathers_Superresolution]]
| Effects of superresolution and magnification on final resolution
|-

| Paper
| [[2021Bouvette_Bisect]]
| Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography
|-

| Paper
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-

| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-

| Paper
| [[2021Efremov_ComaCorrected]]
| Coma-corrected rapid single-particle cryo-EM data collection on the CRYO ARM 300
|-

| Paper
| [[2021Herzik_Setup]]
| Setup for parallel illumination
|-

| Paper
| [[2021Kayama_Multipurpose]]
| Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder
|-

| Paper
| [[2021Lane_NegativeBias]]
| Negative potential bias for faster imaging
|-

| Paper
| [[2021Rheinberger_IceThickness]]
| Scripts to measure ice thickness
|-

| Paper
| [[2021Yang_CRIM]]
| Computer readable image markers (CRIM) for correlative microscopy
|-

| Paper
| [[2021Weis_Strategies]]
| Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
|-

| Paper
| [[2021Wypych_gP2S]]
| LIMS of microscope sessions
|-

| Paper
| [[2021Yang_CLEM]]
| Automated correlative microscopy
|-

| Paper
| [[2021Yonekura_Hole]]
| Automated hole detection using YOLO
|-

| Paper
| [[2022Bepler_Smart]]
| Smart data collection
|-

| Paper
| [[2022Bouvette_SmartScope]]
| SmartScope
|-

| Paper
| [[2022Flutty_bits]]
| Bit-precision for SPA and ET
|-

| Paper
| [[2022Hagen_Screening]]
| Screening of ice thickness using energy filter-based plasmon imaging
|-

| Paper
| [[2022Hohle_Ice]]
| Screening of ice thickness using interferometry
|-

| Paper
| [[2022Peck_200]]
| High-speed high-resolution data collection on a 200 keV cryo-TEM
|-

| Paper
| [[2022Peck_Montage]]
| Montage electron tomography
|-

| Paper
| [[2022Zhu_ElectronCounting]]
| New algorithm for electron counting at the microscope
|-

| Paper
| [[2023Cheng_Leginon]]
| Smart data collection with Leginon
|-

| Paper
| [[2023Kim_Ptolemy]]
| Smart data collection with Ptolemy
|-

| Paper
| [[2023Last_Ice]]
| Measuring the ice thickness with an optical device and a neural network
|-

| Paper
| [[2023Mendez_Pipelines]]
| Evaluation of pipelines for stream processing
|-

| Paper
| [[2024Bobe_Calibration]]
| CryoEM Calibration workflow
|-

| Paper
| [[2024Eisenstein_SPACETomo]]
| Automated acquisition of tilt series
|-

| Conference
| [[2024Fan_RL]]
| Reinforcement learning to optimize the microscope use
|-

| Paper
| [[2024Hatton_EMinsight]]
| EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
|-

| Paper
| [[2024Xu_Miffi]]
| Miffi: automatic classification of micrographs
|-

| Paper
| [[2025Bhandari_Fast]]
| Data acquisition in EPU Fast mode
|-

| Paper
| [[2026Fromm_LowDose]]
| Low dose setup with SerialEM
|-

| Paper
| [[2026Wazny_Damage]]
| Dose damage in nucleic acids
|-

|}

== Single particles ==

=== Automatic particle picking ===

{|

| Paper
| [[1982VanHeel_Detection]]
| Detection of particles in micrographs
|-

| Paper
| [[2001Nicholson_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2001Zhu_Filaments]]
| Automatic identification of filaments in micrographs
|-

| Paper
| [[2004Sigworth_Detection]]
| Classical detection theory and the cryo-EM particle selection problem
|-

| Paper
| [[2004Volkmann_ParticlePicking]]
| An approach to automated particle picking from electron micrographs based on reduced representation templates
|-

| Paper
| [[2004Wong_ParticlePicking]]
| Model-based particle picking for cryo-electron microscopy
|-

| Paper
| [[2004Zhu_Review]]
| Review on automatic particle picking
|-

| Paper
| [[2007Chen_Signature]]
| Automatic particle picking program: Signature
|-

| Paper
| [[2007Woolford_SwarmPS]]
| Automatic particle picking with several criteria, implemented in EMAN Boxer
|-

| Paper
| [[2009Sorzano_MachineLearning]]
| Automatic particle picking based on machine learning of rotational invariants
|-

| Paper
| [[2011Arbelaez_Comparison]]
| Evaluation of the performance of software for automated particle-boxing
|-

| Paper
| [[2013Abrishami_MachineLearning]]
| A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs
|-

| Paper
| [[2013Hauer_2013]]
| Automatic tilt pair detection in Random Conical Tilt
|-

| Paper
| [[2013Hoang_ParallelGPUPicking]]
| Parallel GPU-accelerated particle picking
|-

| Paper
| [[2013Shatsky_ParticlePicking]]
| Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Langlois_ParticlePicking]]
| Automatic particle picking
|-

| Paper
| [[2015Scheres_SemiAutoPicking]]
| Semi-automated selection of cryo-EM particles
|-

| Paper
| [[2016Vilas_AutomaticTilt]]
| Automatic identification of image pairs in untilted-tilted micrograph pairs
|-

| Paper
| [[2016Wang_DeepPicker]]
| A deep learning approach for fully automated particle picking
|-

| Paper
| [[2017Rickgauer_Detection]]
| Picking by correlation
|-

| Paper
| [[2017Zhu_DeepEM]]
| Deep learning approach to picking
|-

| Paper
| [[2018Huber_Helices]]
| Automated tracing of helices
|-

| Paper
| [[2018Heimowitz_ApplePicker]]
| Automated particle picking
|-

| Paper
| [[2018Sanchez_DeepConsensus]]
| Deep learning consensus of multiple automatic pickers
|-

| Paper
| [[2019Alazzawi_Clustering]]
| Use of clustering algorithms to find particles in micrographs
|-

| Paper
| [[2019Bepler_Topaz]]
| Deep learning for particle picking
|-

| Paper
| [[2019Carrasco_IP]]
| Use of standard image processing for particle picking
|-

| Conference
| [[2019Li_Deep]]
| Deep learning for particle picking without box size
|-

| Paper
| [[2019Wagner_Cryolo]]
| Deep learning for particle picking
|-

| Paper
| [[2019Wang_Biobjective]]
| Biobjective function for robust signal detection
|-

| Paper
| [[2019Zhang_Pixer]]
| Deep learning for particle picking
|-

| Paper
| [[2020Sanchez_Cleaner]]
| Deep learning for removing particles from the carbon edges, aggregations, contaminations, ...
|-

| Conference
| [[2021Li_PickerOptimizers]]
| Removal of badly picked particles with Deep Learning
|-

| Paper
| [[2021Ohashi_GRIPS]]
| Two-pass picking with GRIPS
|-

| Paper
| [[2022Eldar_ASOCEM]]
| Automatic segmentation of contaminations
|-

| Conference
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
|-

| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-

| Paper
| [[2022Olek_Icebreaker]]
| Ice thickness detection and its use for particle picking
|-

| Paper
| [[2022Zhang_EPicker]]
| Particle picking based on continual learning
|-

| Paper
| [[2023Dhakal_CryoPPP]]
| A public database for particle picking
|-

| Paper
| [[2023Lucas_Baited]]
| Baited reconstruction with 2D template matching
|-

| Paper
| [[2024Anuk_Auction]]
| Particle picking using combinatorial auction
|-

| Paper
| [[2024Cameron_REPIC]]
| Consensus 2D particle picking using graphs
|-

| Paper
| [[2024Fang_Swin]]
| SwinCryoEM: particle picking
|-

| Paper
| [[2024Gyawali_CryoSegNet]]
| CryoSegNet: particle picking
|-

| Paper
| [[2024Huang_Joint]]
| Joint denoising and picking
|-

| Paper
| [[2025Chung_CRISP]]
| Particle picking with deep learning and Conditional Random Field layers
|-

| Paper
| [[2025Dhakal_Benchmark]]
| Benchmark of particle picking with deep learning
|-

| Paper
| [[2025Neiterman_Frames]]
| Particle picking at the level of frames
|-

| Paper
| [[2025Ni_GTPick]]
| GTPick: Particle picking with deep learning
|-

| Paper
| [[2025Zamanos_CryoEMMAE]]
| Fully unsupervised particle picking using neural networks
|-

| Paper
| [[2025Zhang_2DTMpValue]]
| p-value of the 2D template matching SNR and z-scores
|-

| Paper
| [[2026Balanov_Confirmation]]
| Confirmation bias in particle picking
|-

| Paper
| [[2026He_prismPYP]]
| prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation
|-

|}

=== 2D Preprocessing ===

{|

| Paper
| [[1978Carrascosa_matching]]
| Gray values matching by linear transformations
|-

| Paper
| [[2003Rosenthal_DPR]]
| Contrast enhancement through DPR
|-

| Paper
| [[2004Sorzano_Normalization]]
| Normalization procedures and their statistical properties.
|-

| Paper
| [[2006Sorzano_Denoising]]
| Strong denoising in wavelet space
|-

| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-

| Paper
| [[2012Brilot_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Campbell_Movies]]
| Alignment of beam induced motion in direct detectors
|-

| Paper
| [[2012Zhao_Denoising]]
| Denoising using an invariant Fourier-Bessel eigenspace
|-

| Paper
| [[2013Norousi_Screening]]
| Screening particles to identify outliers
|-

| Paper
| [[2013Bai_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Li_ElectronCounting]]
| Electron counting and beam induced motion correction
|-

| Paper
| [[2013Shigematsu_Movies]]
| Drift correction for movies considering dark field
|-

| Paper
| [[2013Vargas_ParticleQuality]]
| Automatic determination of particle quality
|-

| Paper
| [[2014Scheres_Movies]]
| Beam induced motion correction
|-

| Paper
| [[2015Abrishami_Movies]]
| Alignment of direct detection device micrographs
|-

| Paper
| [[2015Grant_Anisotropic]]
| Automatic estimation and correction of anisotropic magnification
|-

| Paper
| [[2015Grant_OptimalExposure]]
| Filter movies according to the radiation damage
|-

| Paper
| [[2015Rubinstein_Alignment]]
| Frame alignment at the level of particle
|-

| Paper
| [[2015Spear_DoseCompensation]]
| Effect of dose compensation on resolution
|-

| Paper
| [[2015Zhao_AnisotropicMagnification]]
| Correction of anisotropic magnification
|-

| Conference
| [[2016Bajic_Denoising]]
| Denoising and deconvolution of micrographs
|-

| Paper
| [[2016Jensen_RemovalVesicles]]
| Removal of vesicles in membrane proteins
|-

| Paper
| [[2016Bhamre_Denoising]]
| Denoising by 2D covariance estimation
|-

| Paper
| [[2017Berndsen_EMPH]]
| Automated hole masking algorithm
|-

| Paper
| [[2017McLeod_Zorro]]
| Movie alignment by Zorro
|-

| Paper
| [[2017Zheng_MotionCorr2]]
| Movie alignment by MotionCorr2
|-

| Paper
| [[2018Ouyang_Denoising]]
| Denoising based on geodesic distance
|-

| Paper
| [[2018Wu_ContrastEnhancement]]
| Contrast enhancement
|-

| Paper
| [[2019Zivanov_BayesianBIM]]
| Bayesian correction of beam induced movement
|-

| Paper
| [[2020Bepler_TopazDenoise]]
| Preprocessing of micrographs for better picking
|-

| Paper
| [[2020Chung_2SDR]]
| PCA to denoise particles
|-

| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-

| Conference
| [[2020Huang_SuperResolution]]
| Deep learning superresolution combination of frames
|-

| Paper
| [[2020Palovcak_noise2noise]]
| Noise2noise denoising of micrographs
|-

| Paper
| [[2020Strelak_FlexAlign]]
| Continuous deformation model for aligning movie frames
|-

| Conference
| [[2021Fan_Denoising]]
| Particle denoising using vector diffusion maps
|-

| Paper
| [[2022Heymann_ProgressiveSSNR]]
| Progressive SSNR to assess quality and radiation damage
|-

| Paper
| [[2022Shi_Denoising]]
| Contrast estimation and denoising in SPA
|-

| Paper
| [[2023Huang_ZSSR]]
| Multiple image super-resolution, upsampling with deep learning
|-

| Paper
| [[2023Marshall_PCA]]
| Fast PCA on single particle images
|-

| Paper
| [[2023Sharon_Enhancement]]
| Signal enhancement of SPA particles
|-

| Paper
| [[2023Strelak_MovieAlignment]]
| Comparison of movie alignment programs
|-

| Paper
| [[2023Zhang_Denoising]]
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-

| Paper
| [[2024Li_Subtraction]]
| Subtraction of membrane signal in SPA
|-

| Paper
| [[2025Hu_Denoising]]
| A neural network to denoise micrographs
|-

| Paper
| [[2025Yan_Foundation]]
| A comprehensive foundation model for cryo-EM image processing (deep learning)
|-

| Paper
| [[2025Starynska_Membrane]]
| Membrane detection and substraction from micrographs
|-

|}

=== 2D Alignment ===

{|

| Paper
| [[1981Frank_Averaging]]
| 2D averaging and phase residual
|-

| Paper
| [[1982Saxton_Averaging]]
| 2D averaging using correlation
|-

| Paper
| [[1998Sigworth_ML2D]]
| Maximum likelihood alignment in 2D
|-

| Paper
| [[2003Cong_FRM2D]]
| Fast Rotational Matching in 2D
|-

| Paper
| [[2005Cong_FRM2D]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2016Aguerrebere_Limits]]
| Fundamental limits of 2D translational alignment
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-

| Paper
| [[2019Radermacher_Correlation]]
| On the properties of cross correlation for the alignment of images
|-

| Paper
| [[2020Lederman_representation]]
| A representation theory perspective of alignment and classification
|-

| Paper
| [[2020Marshall_Invariants]]
| Recovery of an image from its invariants
|-

| Paper
| [[2021Chen_Fast]]
| Fast alignment through Power Spectrum
|-

| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-

| Paper
| [[2021Heimowitz_Centering]]
| Centering noisy images
|-

| Conference
| [[2022Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bendory_Complexity]]
| Computational complexity of multireference image alignment
|-

| Paper
| [[2024Bai_NUFT]]
| 2D Image classification based on the Non-uniform Fourier Transform
|-

| Paper
| [[2025Kapnulin_Outlier]]
| 2D Outlier rejection based on radial averages
|-

| Paper
| [[2025Tang_CryoLike]]
| CryoLike: a library for fast image comparison
|-

| Paper
| [[2025Balanov_Confirmation]]
| Confirmation bias in GMMs
|-

| Paper
| [[2026Balanov_Einstein]]
| Statistical analysis of the Einstein from noise phenomenon
|-

|}

=== 2D Classification and clustering ===

{|

| Paper
| [[1981VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[1984VanHeel_MSA]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2005Scheres_ML2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2010Sorzano_CL2D]]
| Multireference alignment and classification in 2D
|-

| Paper
| [[2011Singer_DiffusionMaps]]
| Classification in 2D based on graph analysis of the projections
|-

| Paper
| [[2012Yang_ISAC]]
| Iterative Stable Alignment and clustering
|-

| Paper
| [[2014Sorzano_Outlier]]
| Outlier detection in 2D classifications.
|-

| Paper
| [[2014Zhao_Aspire]]
| Fast classification based on rotational invariants and vector diffusion maps
|-

| Paper
| [[2015Huang_Robust]]
| Robust w-estimators of 2D classes
|-

| Paper
| [[2016Kimanius_Accelerated]]
| GPU Accelerated image classification and high-resolution refinement
|-

| Paper
| [[2016Reboul_Stochastic]]
| Stochastic Hill Climbing for calculating 2D classes
|-

| Conference
| [[2017Bhamre_Mahalanobis]]
| 2D classification using Mahalanobis distance
|-

| Paper
| [[2017Wu_GTM]]
| 2D classification using Generative Topographic Mapping
|-

| Conference
| [[2018Boumal_SinglePass]]
| Single pass classification
|-

| Conference
| [[2018Shuo_Network]]
| 2D Clustering by network metrics
|-

| Paper
| [[2020Ma_RotationInvariant]]
| 2D heterogeneity determination by rotation invariant features
|-

| Conference
| [[2020Miolane_VAEGAN]]
| 2D Analysis by deep learning
|-

| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-

| Paper
| [[2022Vilela_Feret]]
| 2D heterogeneity detection through Feret signatures
|-

| Paper
| [[2022Wang_Spectral]]
| 2D classification with spectral clustering
|-

| Paper
| [[2022Zhang_DRVAE]]
| 2D classification with deep learning and K-means++
|-

| Paper
| [[2023Chen_Joint]]
| 2D classification with deep learning and joint unsupervised difference learning
|-

| Conference
| [[2023Weiss_Noise]]
| Identifying non-particles with probabilistic PCA
|-

| Paper
| [[2024Tang_SimCryoCluster]]
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-

| Paper
| [[2025Bai_NUDFT]]
| 2D Classification in SPA using the Non-uniform DFT
|-

|}

=== 3D Alignment ===

{|

| Paper
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[1986Goncharov_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1987VanHeel_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[1988Provencher_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1988Radermacher_RCT]]
| Random Conical Tilt and Single axis tilt
|-

| Paper
| [[1988Vogel_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[1990Gelfand_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Goncharov_Moments]]
| Angular assignment using moments (reference free)
|-

| Paper
| [[1990Harauz_Quaternions]]
| Use of quaternions to represent rotations
|-

| Paper
| [[1994Penczek_Real]]
| Angular assignment using projection matching in real space
|-

| Paper
| [[1994Radermacher_Radon]]
| Angular assignment in Radon space
|-

| Paper
| [[1996Penczek_CommonLines]]
| Angular assignment using common lines (reference free)
|-

| Paper
| [[2003Rosenthal_DPR]]
| Angular assignment using DPR
|-

| Paper
| [[2004Sorzano_Wavelet]]
| Angular assignment in the wavelet space.
|-

| Paper
| [[2005Jonic_Splines]]
| Angular assignment in Fourier space using spline interpolation.
|-

| Paper
| [[2005Yang_Simultaneous]]
| Simultaneaous alignment and reconstruction
|-

| Paper
| [[2006Ogura_SimulatedAnnealing]]
| Angular asignment by simulated annealing
|-

| Paper
| [[2007Grigorieff_Continuous]]
| Continuous angular assignment in Fourier space
|-

| Paper
| [[2010Jaitly_Bayesian]]
| Angular assignment by a Bayesian method and annealing
|-

| Paper
| [[2010Sanz_Random]]
| Random model method
|-

| Paper
| [[2010Singer_Voting]]
| Detecting consistent common lines by voting (reference free)
|-

| Paper
| [[2011Singer_SDP]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-

| Paper
| [[2012Giannakis_Scattering]]
| Construction of an initial volume, reference free, by graph analysis of the projections
|-

| Paper
| [[2012Shkolnisky_Sync]]
| Angular assignment by synchronization of rotations (reference free)
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Wang_LUD]]
| Angular assignment by least unsquared deviations (reference free)
|-

| Paper
| [[2014Vargas_RANSAC]]
| Initial model using RANSAC (reference free)
|-

| Paper
| [[2015Joubert_Pseudoatoms]]
| Initial model based on pseudo-atoms
|-

| Paper
| [[2015Singer_Kam]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-

| Paper
| [[2015Sorzano_Significant]]
| Statistical approach to the initial volume estimation (reconstruct significant)
|-

| Paper
| [[2016Cossio_BayesianGPU]]
| GPU implementation of the Bayesian 3D reconstruction approach
|-

| Conference
| [[2016Michels_Heterogeneous]]
| Initial volume in the presence of heterogeneity
|-

| Paper
| [[2016Pragier_Graph]]
| Graph partitioning approach to angular reconstitution
|-

| Paper
| [[2017Greenberg_CommonLines]]
| Common lines for reference free ab-initio reconstruction
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2019Zehni_Joint]]
| Continuous angular refinement and reconstruction
|-

| Paper
| [[2020Sharon_NonUniformKam]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-

| Paper
| [[2020Xie_Network]]
| Angular assignment considering a network of assignments
|-

| Paper
| [[2021Jimenez_DeepAlign]]
| Angular alignment using deep learning
|-

| Paper
| [[2021Kojima_Preferred]]
| Identification of preferred orientations
|-

| Conference
| [[2021Nashed_CryoPoseNet]]
| CryoPoseNet: Angular alignment with deep learning
|-

| Conference
| [[2021Zhong_CryoDRGN2]]
| CryoDRGN2: Angular alignment with deep learning
|-

| Conference
| [[2022Levy_CryoAI]]
| CryoAI: Angular assignment through neural network
|-

| Paper
| [[2022Lian_Neural]]
| Angular assignment through neural network
|-

| Paper
| [[2022Lu_SphericalEmbeddings]]
| Angular assignment through common lines and spherical embeddings
|-

| Paper
| [[2022Wang_Thunder]]
| Angular assignment implementation in GPU
|-

| Conference
| [[2023Cesa_Alignment]]
| 3D alignment based on deep learning and equivariant representations
|-

| Paper
| [[2023Harpaz_Alignment]]
| Fast alignment of two maps using common lines
|-

| Paper
| [[2023Ling_Synch]]
| Synchronization of projection directions
|-

| Paper
| [[2023Rangan_Fast]]
| Fast angular assignment using Fourier-Bessel
|-

| Paper
| [[2023Riahi_Transport]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Chung_CryoForum]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-

| Paper
| [[2024Muller_Common]]
| Initial volume in the presence of heterogeneity using common lines
|-

| Paper
| [[2024Nottelet_Feret]]
| Feret signature to detect preferred orientations and misclassified images
|-

| Paper
| [[2024Sanchez_Cesped]]
| CESPED: A benchmark for supervised particle pose estimation
|-

| Conference
| [[2024Shekarforoush_CryoSPIN]]
| CryoSpin: Semi-amortized image alignment using deep learning
|-

| Paper
| [[2024Singer_Wasserstein]]
| Alignment of two 3D maps using Wasserstein's distance
|-

| Paper
| [[2024Titarenko_optimal]]
| Optimal 3D angular sampling
|-

| Paper
| [[2024Wang_CommonLines]]
| 3D Alignment by common lines
|-

| Paper
| [[2024Zhang_Kam]]
| Distance between maps without aligning them
|-

| Paper
| [[2026Van_Polar]]
| A polar Fourier approach to the initial volume problem
|-

| Paper
| [[2026Xu_MMSE]]
| Minimum Mean-Square error estimation of the particle orientation
|-

|}

=== 3D Reconstruction ===
{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1980Kam_SphericalHarmonics]]
| 3D Reconstruction using spherical harmonics
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1986Harauz_FBP]]
| Exact filters for Filtered Back Projection
|-

| Chapter
| [[1992Radermacher_WBP]]
| Exact filters for Weighted Back Projection
|-

| Paper
| [[1997Zhu_RecCTF]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-

| Paper
| [[1998Boisset_Uneven]]
| Artifacts in SIRT and WBP under uneven angular distributions
|-

| Paper
| [[1998Marabini_ART]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-

| Paper
| [[2001Sorzano_Uneven]]
| Free parameter selection under uneven angular distributions
|-

| Paper
| [[2005Sorzano_Parameters]]
| Free parameter selection for optimizing multiple tasks
|-

| Paper
| [[2008Sorzano_Constraints]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-

| Paper
| [[2009Bilbao_ParallelART]]
| Efficient parallelization of ART
|-

| Paper
| [[2011Li_GradientFlow]]
| Regularized 3D Reconstruction by Gradient Flow
|-

| Paper
| [[2011Vonesch_Wavelets]]
| Fast wavelet-based 3D reconstruction
|-

| Paper
| [[2012Gopinath_ShapeRegularization]]
| Regularized 3D Reconstruction by Shape information
|-

| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| 3D reconstruction in an adaptive basis promoting sparsity
|-

| Paper
| [[2012Sindelar_NoiseReduction]]
| Optimal noise reduction in 3D reconstructions
|-

| Paper
| [[2013Elmlund H_PRIME]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-

| Paper
| [[2013Lyumkis_Optimod]]
| Construction of initial volumes with Optimod
|-

| Paper
| [[2013Wang FIRM]]
| Fast 3D reconstruction in Fourier domain
|-

| Paper
| [[2014Kunz_SART_OS]]
| Simultaneous ART with OS
|-

| Paper
| [[2015Abrishami_Fourier]]
| 3D Reconstruction in Fourier space
|-

| Paper
| [[2015Dvornek_SubspaceEM]]
| Fast Maximum a posteriori
|-

| Paper
| [[2015Moriya_Bayesian]]
| Bayesian approach to suppress limited angular artifacts
|-

| Paper
| [[2015Xu_GeometricFlow]]
| Multi-scale geometric flow
|-

| Arxiv
| [[2016Ye_Cohomology]]
| Cohomology properties of 3D reconstruction
|-

| Paper
| [[2017Barnett_Marching]]
| Initial volume through frequency marching
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Punjani_CryoSPARCTheory]]
| Theory related to CryoSPARC
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bartesaghi_Refinement]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-

| Paper
| [[2018Hu_ParticleFilter]]
| A particle filter framework for 3D reconstruction
|-

| Conference
| [[2018Levin_Kam]]
| Ab initio reconstruction by autocorrelation analysis
|-

| Conference
| [[2018Michels_RBF]]
| Ab-initio reconstruction with radial basis functions
|-

| Paper
| [[2018Reboul_Simple]]
| Ab initio reconstruction with Simple
|-

| Paper
| [[2018Sorzano_Highres]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-

| Paper
| [[2018Sorzano_Swarm]]
| Consensus of several initial volumes by swarm optimization
|-

| Paper
| [[2018Zhu_Ewald]]
| 3D Reconstruction with Ewald sphere correction
|-

| Paper
| [[2019Gomez_Initial]]
| Construction of initial models
|-

| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-

| Paper
| [[2019Wilkinson_Scales]]
| Combining data acquired at different scales
|-

| Paper
| [[2020Alazzawi_Auto]]
| Automatic full processing of micrographs to yield a 3D reconstruction
|-

| Paper
| [[2020Pan_TV]]
| 3D Reconstruction with total variation regularization
|-

| Paper
| [[2020Punjani_NonUniform]]
| Non-uniform refinement
|-

| Paper
| [[2020Ramlaul_Sidesplitter]]
| Local filtering along the reconstruction iterations
|-

| Paper
| [[2020Xie_Automatic]]
| Automatic 3D reconstruction from projections
|-

| Conference
| [[2020Venkatakrishnan_MBIR]]
| Model based image reconstruction
|-

| Paper
| [[2020Zhou_AutomaticSelection]]
| Automatic selection of particles for 3D reconstruction
|-

| Paper
| [[2021Abrishami_Localized]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-

| Paper
| [[2021Gupta_CryoGAN]]
| 3D Reconstruction via Generative Adversarial Learning
|-

| Paper
| [[2021Luo_Opus]]
| 3D Reconstruction with a sparse and smoothness constraint
|-

| Paper
| [[2021Kimanius_PriorKnowledge]]
| Incorporation of prior knowledge during 3D reconstruction
|-

| Paper
| [[2021Sorzano_Uneven]]
| Algorithmic robustness to uneven angular distributions
|-

| Paper
| [[2022Havelkova_regularization]]
| Regularization of iterative reconstruction algorithms
|-

| Conference
| [[2022Kimanius_Sparse]]
| Sparse Fourier backpropagation
|-

| Paper
| [[2022Lan_RCT]]
| Random Conical Tilt without picking
|-

| Paper
| [[2023Bendory_Autocorrelation]]
| Initial volume through autocorrelation analysis with sparsity constraints
|-

| Paper
| [[2023Geva_AbInitio]]
| Initial volume through common lines for tetahedral and octahedral symmetry
|-

| Paper
| [[2023Herreros_ZART]]
| Correction of continuous heterogeneity during the 3D reconstruction
|-

| Paper
| [[2023Rangan_AbInitio]]
| Robust ab initio reconstruction
|-

| Paper
| [[2023Zhu_CryoSieve]]
| CryoSieve: Selection of the best particles to reconstruct
|-

| Paper
| [[2024Aiyer_Workflow]]
| Workflow for the reconstruction of tilted samples
|-

| Paper
| [[2024Huang_CryoNefen]]
| 3D reconstruction in real space with neural networks
|-

| Paper
| [[2024Liu_kinetic]]
| A kinetic model for the resolution of the initial model using common lines
|-

| Paper
| [[2024Suder_Workflow]]
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-

| Paper
| [[2024Zhu_SIRM]]
| Reconstruction strategy and weights to fight preferred orientations
|-

| Paper
| [[2025Liu_SpIsonet]]
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-

| Paper
| [[2025Singh_Mismatch]]
| Image processing workflow to address particles with symmetry mismatches
|-

| Paper
| [[2025Toader_SGD]]
| 3D Reconstruction with Stochastic Gradient Descent
|-

| Paper
| [[2025Van_Probabilistic]]
| Multireference initial volume reconstruction in SPA
|-

| Paper
| [[2025Woollard_InstaMap]]
| InstaMap: 3D reconstruction using neural networks
|-

| Conference
| [[2025Zhang_CryoFastAR]]
| CryoFastAR: initial volume with neural networks
|-

| Paper
| [[2025Zhang_CryoPROS]]
| CryoPROS: correcting misalignment caused by preferred orientation
|-

| Paper
| [[2026Barchet_NonUniform]]
| Explicit correction of severely non-uniform distributions in SPA
|-

| Paper
| [[2026Chen_MPM]]
| Masked projection modelling for 3D reconstruction
|-

|}

=== 3D Heterogeneity ===

{|

| Paper
| [[2004White_Size]]
| Heterogeneity classification of differently sized images
|-

| Paper
| [[2006Penczek_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Scheres_ML3D]]
| Maximum Likelihood alignment and classification in 3D
|-

| Paper
| [[2008Herman_Graph]]
| Classification by graph partitioning
|-

| Paper
| [[2009Spahn_Bootstrap]]
| 3D heterogeneity through bootstrap
|-

| Paper
| [[2010Elmlund_AbInitio]]
| Solving the initial volume problem with multiple conformations
|-

| Paper
| [[2010Shatsky_MultiVariate]]
| Multivariate Statistical Analysis
|-

| Paper
| [[2012Scheres_Bayesian]]
| A Bayesian view on cryo-EM structure determination
|-

| Paper
| [[2012Zheng_Covariance]]
| Estimation of the volume covariance
|-

| Paper
| [[2013Wang_MLVariance]]
| Maximum Likelihood estimate of the map variance
|-

| Paper
| [[2013Lyumkis D_FREALIGN]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-

| Paper
| [[2014Chen_Migration]]
| Particle migration analysis in 3D classification
|-

| Paper
| [[2014Dashti_Brownian]]
| Continuous heterogeneity through Brownian trajectories
|-

| Paper
| [[2014Schwander_manifold]]
| Continuous heterogeneity through Manifold Analysis
|-

| Paper
| [[2014Jin_NMA]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-

| Paper
| [[2015Anden_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Bai_Focused]]
| Focused classification
|-

| Paper
| [[2015Katsevich_Covariance]]
| 3D Covariance matrix estimation for heterogeneity
|-

| Paper
| [[2015Klaholz_MRA]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-

| Paper
| [[2015Liao_Covariance]]
| Estimation of the 3D covariance from 2D projections
|-

| Paper
| [[2015Tagare_Direct]]
| Direct reconstruction of PCA components
|-

| Paper
| [[2016Gong_Mechanical]]
| Mechanical model for macromolecules
|-

| Paper
| [[2016Rawson_Movement]]
| Movement and flexibility
|-

| Paper
| [[2016Shan_Multibody]]
| Multibody refinement
|-

| Paper
| [[2016Sorzano_StructMap]]
| Sorting a discrete set of conformational states
|-

| Paper
| [[2016Sorzano_Strain]]
| Calculate local stretches, strains and rotations from two conformational states
|-

| Paper
| [[2017Punjani_CryoSPARC]]
| CryoSPARC
|-

| Paper
| [[2017Schillbach_Warpcraft]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-

| Paper
| [[2018Anden_Covariance]]
| Structural Variability from Noisy Tomographic Projections
|-

| Paper
| [[2018Haselbach_FreeEnergy]]
| Analysis of the free energy landscape through PCA
|-

| Paper
| [[2018Nakane_MultiBody]]
| Structural Variability through multi-body refinement
|-

| Paper
| [[2019Serna_Review]]
| Review of classification tools
|-

| Paper
| [[2018Solernou_FFEA]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Zhang_Local]]
| Local variability and covariance
|-

| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-

| Conference
| [[2020Gupta_MultiCryoGAN]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-

| Paper
| [[2020Harastani_NMA]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-

| Paper
| [[2020Maji_Propagation]]
| Propagation of conformational coordinates across angular space
|-

| Paper
| [[2020Moscovich_DiffusionMaps]]
| Heterogeneity analysis by diffusion maps and spectral volumes
|-

| Paper
| [[2020Seitz_Polaris]]
| Analysis of energy landscapes to find minimal action paths
|-

| Conference
| [[2020Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2020Verbeke_Separation]]
| Heterogeneity analysis by comparing common lines
|-

| Paper
| [[2021Chen_GM]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-

| Paper
| [[2021Giraldo_cryoBIFE]]
| A Bayesian approach to extracting free‑energy profiles
|-

| Conference
| [[2021Hamitouche_NMADL]]
| Continuous heterogeneity analysis through normal modes and deep learning
|-

| Paper
| [[2021Herreros_Zernikes3D]]
| Continuous heterogeneity analysis through Zernikes 3D
|-

| Paper
| [[2021Kazemi_Enrich]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-

| Paper
| [[2021Matsumoto_DEFmap]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-

| Chapter
| [[2021Nakasako_Landscape]]
| Estimation of free-energy landscape from images
|-

| Paper
| [[2021Punjani_3DVA]]
| 3D Variability analysis from images
|-

| Paper
| [[2021Sorzano_PCA]]
| PCA is limited to low-resolution
|-

| Paper
| [[2021Zhong_CryoDRGN]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-

| Paper
| [[2022Arnold_liganded]]
| Test to see if liganded states can be detected
|-

| Paper
| [[2022Ecoffet_MorphOT]]
| More physically plausible morphing between two states
|-

| Paper
| [[2022Gomez_Hierarchical]]
| Hierarchical classification of particles
|-

| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-

| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-

| Paper
| [[2022Rabuck_Quant]]
| Workflow for discrete heterogeneity analysis
|-

| Paper
| [[2022Seitz_ESPER]]
| ESPER through manifold embeddings
|-

| Paper
| [[2022Skalidis_Endogenous]]
| AI tools to recognize proteins in cellular fractions
|-

| Paper
| [[2022Wu_Manifold]]
| Continuous heterogeneity through manifold learning
|-

| Paper
| [[2022Zhou_Data]]
| Determination of the number of discrete 3D classes
|-

| Paper
| [[2023Barchet_Focused]]
| Applications and strategies in focused classification and refinement
|-

| Paper
| [[2023Afonine_Varref]]
| Phenix.varref for the analysis of the model heterogeneity
|-

| Paper
| [[2023Chen_GMM]]
| Continuous heterogeneity analysis with GMMs and neural networks
|-

| Paper
| [[2023Dsouza_benchmark]]
| Benchmark analysis of various continuous heterogeneity algorithms
|-

| Paper
| [[2023Esteve_Spectral]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-

| Paper
| [[2023Fernandez_Subtraction]]
| Subtraction of unwanted signals to improve classification and alignment
|-

| Paper
| [[2023Forsberg_Filter]]
| Filter to estimate the local heterogeneity
|-

| Paper
| [[2023Herreros_Hub]]
| Flexibility hub: an integrative platform for continuous heterogeneity
|-

| Paper
| [[2023Luo_OpusDSD]]
| OPUS DSD: a neural network approach to continuous heterogeneity
|-

| Paper
| [[2023Kinman_Analysis]]
| Analysis of the continuous heterogeneity results of CryoDrgn
|-

| Paper
| [[2023Matsumoto_DEFmap]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-

| Paper
| [[2023Punjani_3DFlex]]
| Continuous heterogeneity through 3DFlex
|-

| Paper
| [[2023Seitz_Geometric]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-

| Paper
| [[2023Seitz_ESPER]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-

| Paper
| [[2023Tang_Reweighting]]
| Ensemble reweighting using Cryo-EM particles
|-

| Paper
| [[2023Vuillemot_MDSPACE]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-

| Paper
| [[2023Wang_Autoencoder]]
| Discrete heterogeneity based on autoencoders
|-

| Paper
| [[2024Amisaki_Multilevel]]
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-

| Paper
| [[2024Chen_Focused]]
| Focused reconstruction of heterogeneous macromolecules
|-

| Paper
| [[2024Fan_CryoTrans]]
| CryoTrans: Trajectory generation between two states
|-

| Paper
| [[2024Klindt_Disentanglement]]
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-

| Conference
| [[2024Levy_Hydra]]
| Hydra: Continuous and discrete heterogeneity using neural fields
|-

| Paper
| [[2024Li_CryoStar]]
| CryoStar: Continuous heterogeneity analysis with structural priors
|-

| Paper
| [[2024Schwab_DynaMight]]
| DynaMight: Heterogeneity analysis using neural networks
|-

| Paper
| [[2024Shi_Priors]]
| Latent space priors for continuous heterogeneity
|-

| Paper
| [[2024Song_RMSFNet]]
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-

| Paper
| [[2024Yoshidome_4D]]
| Heterogeneity analysis using molecular dynamics
|-

| Paper
| [[2025Chen_GMM]]
| Continuous heterogeneity analysis in SPA using atomic models
|-

| Paper
| [[2025Dingeldein]]
| Amortized template matching using simulation-based inference
|-

| Paper
| [[2025Herreros_HetSiren]]
| Discrete and Continuous heterogeneity analysis using neural networks
|-

| Paper
| [[2025Gilles_Covariance]]
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-

| Paper
| [[2025Kinman_SIREN]]
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-

| Paper
| [[2025Lauzirika_Distinguishable]]
| How many (distinguishable) classes can we identify in single-particle analysis?
|-

| Paper
| [[2025Levy_CryoDRGNAI]]
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-

| Paper
| [[2026Chen_3DDF-VAE]]
| 3DDF-VAE: Identification of rare conformations in SPA
|-

| Paper
| [[2026Evans_Counting]]
| Counting particles in cryo-electron microscopy may result in incorrect population estimates.
|-

| Conference
| [[2026Gao_CryoKRAQEN]]
| CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction
|-

| Paper
| [[2026Silva_CryoJax]]
| CryoJax Ensemble: Continuous heterogeneity analysis using atomic models
|-

|}

=== Validation ===

{|

| Paper
| [[2008Stagg_TestBed]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-

| Paper
| [[2011Henderson]]
| Tilt Validation
|-

| Paper
| [[2011Read]]
| Validation of PDBs
|-

| Paper
| [[2012Henderson]]
| EM Map Validation
|-

| Paper
| [[2013Cossio_Bayesian]]
| EM Map Validation in a probabilistic setting
|-

| Paper
| [[2013Chen_NoiseSubstitution]]
| Noise substitution at high resolution for measuring overfitting
|-

| Paper
| [[2013Ludtke_Validation]]
| Structural validation, example of the Calcium release channel
|-

| Paper
| [[2013Murray_Validation]]
| Validation of a 3DEM structure through a particular example
|-

| Paper
| [[2014Russo_StatisticalSignificance]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-

| Paper
| [[2014Stagg_Reslog]]
| EM Map Validation through the resolution evolution with the number of particles
|-

| Paper
| [[2014Wasilewski_Tilt]]
| Web implementation of the tilt pair validation
|-

| Paper
| [[2015Heymann_Alignability]]
| EM Map Validation through the resolution of reconstructions from particles and noise
|-

| Paper
| [[2015Oliveira_FreqLimited]]
| Comparison of gold standard and frequency limited optimization
|-

| Paper
| [[2015Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2015Wriggers_Secondary]]
| Validation by secondary structure
|-

| Paper
| [[2016Degiacomi_IM]]
| Comparison of Ion Mobility data and EM volumes
|-

| Paper
| [[2016Kim_SAXS]]
| Comparison of SAXS data and EM projections
|-

| Paper
| [[2016Rosenthal_Review]]
| Review of validation methods
|-

| Paper
| [[2016Vargas_Alignability]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-

| Paper
| [[2017Monroe_PDBRefinement]]
| Validation by comparison to a refined PDB
|-

| Paper
| [[2018Afonine_Phenix]]
| Tools in Phenix for the validation of EM maps
|-

| Paper
| [[2018Heymann_Bsoft]]
| Map validation using Bsoft
|-

| Paper
| [[2018Heymann_Challenge]]
| A summary of the assessments of the 3D Map Challenge
|-

| Paper
| [[2018Jonic_Gaussian]]
| Assessment of sets of volumes by pseudoatomic structures
|-

| Paper
| [[2018Naydenova_AngularDistribution]]
| Evaluating the angular distribution of a 3D reconstruction
|-

| Paper
| [[2018Pages_Symmetry]]
| Looking for a symmetry axis in a PDB
|-

| Paper
| [[2018Pintilie_SSE]]
| Evaluating the quality of SSE and side chains
|-

| Paper
| [[2019Herzik_Multimodel]]
| Local and global quality by multi-model fitting
|-

| Paper
| [[2020Chen_Atomic]]
| Validation of the atomic models derived from CryoEM
|-

| Paper
| [[2020Cossio_CrossValidation]]
| Need for cross validation
|-

| Paper
| [[2020Ortiz_CrossValidation]]
| Cross validation for SPA
|-

| Paper
| [[2020Sazzed_helices]]
| Validation of helix quality
|-

| Paper
| [[2020Stojkovic_PTM]]
| Validation of post-translational modifications
|-

| Paper
| [[2020Tiwari_PixelSize]]
| Fine determination of the pixel size
|-

| Paper
| [[2021Mendez_Graph]]
| Identification of incorrectly oriented particles
|-

| Paper
| [[2021Pintilie_Validation]]
| Review of map validation approaches
|-

| Paper
| [[2021Olek_FDR]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-

| Paper
| [[2022Garcia_DeepHand]]
| Checking the correct handedness with a neural network
|-

| Paper
| [[2022Sorzano_Bias]]
| Bias, variance, gold-standard and overfitting in SPA
|-

| Paper
| [[2022Sorzano_Validation]]
| Validation scheme and server for SPA
|-

| Paper
| [[2022Terashi_DAQ]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2022Waarshamanage_EMDA]]
| Validation of models fitted into CryoEM maps
|-

| Paper
| [[2024Feng_DeepQs]]
| DeepQ: Local quality of the map
|-

| Paper
| [[2024Jeon_CryoBench]]
| Datasets for heterogeneity benchmarking
|-

| Paper
| [[2024Lytje_SAXS]]
| Validation of CryoEM maps with SAXS curves
|-

| Paper
| [[2024Sanchez_Anisotropy]]
| New measure of anisotropy in maps
|-

| Paper
| [[2024Verbeke_SelfFSC]]
| Self FSC: FSC with a single map
|-

| Paper
| [[2025Bromberg_Hand]]
| Handedness validation based on the Ewald sphere
|-

| Paper
| [[2025Pintilie_QScore]]
| Extension of Q-Score to analyze SPA maps
|-

| Paper
| [[2026Urzhumtsev_ProjDir]]
| Quantification of the uniformity of projection direction distributions
|-

|}

=== Resolution ===

{|

| Paper
| [[1986Harauz_FBP]]
| Fourier Shell Correlation
|-

| Paper
| [[1987Unser_SSNR]]
| 2D Spectral Signal to Noise Ratio
|-

| Paper
| [[2002Penczek_SSNR]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-

| Paper
| [[2003Rosenthal_DPR]]
| Review of the FSC and establishment of a new threshold
|-

| Paper
| [[2005Unser_SSNR]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-

| Paper
| [[2005VanHeel_FSC]]
| Establishment of a new threshold for FSC
|-

| Paper
| [[2007Sousa_AbInitio]]
| Resolution measurement on neighbour Fourier voxels
|-

| Paper
| [[2014Kucukelbir_Local]]
| Quantifying the local resolution of cryo-EM density maps
|-

| Paper
| [[2016Pintilie_Probabilistic]]
| Probabilistic models and resolution
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Statistical properties of resolution measures defined in Fourier space
|-

| Conference
| [[2018Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2018Carugo_BFactors]]
| How large can B-factors be in protein crystals
|-

| Conference
| [[2018Kim_FourierError]]
| Comparison between a gold standard and a reconstruction
|-

| Paper
| [[2018Rupp_Problems]]
| Problems of resolution as a proxy number for map quality
|-

| Paper
| [[2018Vilas_MonoRes]]
| Local resolution by monogenic signals
|-

| Paper
| [[2018Yang_Multiscale]]
| Resolution from a multiscale spectral analysis
|-

| Paper
| [[2019Avramov_DeepLearning]]
| Deep learning classification of volumes into low, medium and high resolution
|-

| Paper
| [[2019Heymann_Statistics]]
| SNR, FSC, and related statistics
|-

| Paper
| [[2019Ramirez_DeepRes]]
| Resolution determination by deep learning
|-

| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| Resolution attenuation through non-uniform Fourier sampling
|-

| Paper
| [[2020Beckers_Permutation]]
| Permutation tests for the FSC
|-

| Paper
| [[2020Penczek_mFSC]]
| Modified FSC to avoid mask induced artifacts
|-

| Paper
| [[2020Vilas_MonoDir]]
| Local and directional resolution
|-

| Paper
| [[2023Dai_CryoRes]]
| Local resolution through deep learning
|-

| Paper
| [[2023Vilas_FSO]]
| Fourier Shell Occupancy to measure anisotropy
|-

| Paper
| [[2025Urzhumtsev_RescaleFSC]]
| Rescaling of the FSC
|-

|}

=== Sharpening of high resolution information ===
{|

| Paper
| [[2003Rosenthal_DPR]]
| Contrast restoration and map sharpening
|-

| Paper
| [[2008Fernandez_Bfactor]]
| Bfactor determination and restoration
|-

| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| Phase retrieval or extension
|-

| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| Spherical deconvolution
|-

| Paper
| [[2015Spiegel_VISDEM]]
| Visualization improvement by the use of pseudoatomic profiles
|-

| Paper
| [[2016Jonic_Pseudoatoms]]
| Approximation with pseudoatoms
|-

| Paper
| [[2016Jonic_Denoising]]
| Denoising and high-frequency boosting by pseudoatom approximation
|-

| Paper
| [[2017Jakobi_LocScale]]
| Sharpening based on an atomic model
|-

| Paper
| [[2019Ramlaul_Filtering]]
| Local agreement filtering (denoising)
|-

| Conference
| [[2020Mullick_SuperResolution]]
| Superresolution from a map
|-

| Paper
| [[2020Ramirez_LocalDeblur]]
| Local deblur (local Wiener filter)
|-

| Paper
| [[2020Terwilliger_density]]
| Density modification of CryoEM maps
|-

| Paper
| [[2020Vilas_Bfactor]]
| Global B-factor correction does not represent macromolecules
|-

| Paper
| [[2021Beckers_Interpretation]]
| Improvements from the raw reconstruction to a structure to model
|-

| Paper
| [[2021Kaur_LocSpiral]]
| LocSpiral, LocBsharpen, LocBfactor
|-

| Paper
| [[2021Fernandez_Adjustment]]
| Map adjustment for subtraction, consensus and sharpening
|-

| Paper
| [[2021Sanchez_DeepEMhancer]]
| Deep learning algorithm for volume restoration
|-

| Paper
| [[2022Gilles_Wilson]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-

| Paper
| [[2022Vargas_tubular]]
| Map enhancement by multiscale tubular filter
|-

| Paper
| [[2023He_EMReady]]
| Map enhancement with local and non-local deep learning (EMReady)
|-

| Paper
| [[2023Maddhuri_EMGan]]
| Map enhancement with GANs (EMGan)
|-

| Paper
| [[2024Agarwal_crefDenoiser]]
| cRefDenoiser: map denoising based on deep learning
|-

| Paper
| [[2024Kimanius_Blush]]
| Blush: data-driven regularization
|-

| Paper
| [[2025Selvaraj_CryoTEN]]
| CryoTEN: map enhancement using transformers
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[1982Schiske_Correction]]
| CTF correction for tilted objects
|-

| Paper
| [[1988Toyoshima_Model]]
| CTF estimation
|-

| Paper
| [[1995Frank_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1996Skoglund_MaxEnt]]
| CTF correction with Maximum Entropy
|-

| Paper
| [[1996Zhou_Model]]
| CTF model and user interface for manual fitting
|-

| Paper
| [[1997Fernandez_AR]]
| PSD estimation using periodogram averaging and AR models
|-

| Paper
| [[1997Penczek_Wiener]]
| CTF correction using Wiener filter
|-

| Paper
| [[1997Stark_Deconvolution]]
| CTF correction using deconvolution
|-

| Paper
| [[1997Zhu_RecCTF]]
| CTF correction and reconstruction
|-

| Paper
| [[2000DeRosier_EwaldCorrection]]
| CTF correction considering the Ewald sphere
|-

| Paper
| [[2000Jensen_TiltedCorrection]]
| CTF correction considering tilt in backprojection
|-

| Paper
| [[2001Saad_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Huang_CTFEstimate]]
| CTF estimation
|-

| Paper
| [[2003Mindell_CTFTILT]]
| CTF estimation for tilted micrographs
|-

| Paper
| [[2003Sander_MSA]]
| CTF estimation through MSA classification of PSDs
|-

| Paper
| [[2003Velazquez_ARMA]]
| PSD and CTF estimation using ARMA models
|-

| Paper
| [[2004Sorzano_IDR]]
| CTF restoration and reconstruction with Iterative Data Refinement
|-

| Conference
| [[2004Wan_CTF]]
| Spatially variant CTF
|-

| Paper
| [[2004Zubelli_Chahine]]
| CTF restoration and reconstruction with Chahine's multiplicative method
|-

| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-

| Paper
| [[2005Mallick_ACE]]
| CTF estimation
|-

| Paper
| [[2006Wolf_Ewald]]
| CTF correction considering Ewald sphere
|-

| Paper
| [[2007Jonic_EnhancedPSD]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-

| Paper
| [[2007Philippsen_Model]]
| CTF Model for tilted specimens
|-

| Paper
| [[2007Sorzano_CTF]]
| CTF estimation using enhanced PSDs
|-

| Paper
| [[2009Sorzano_Sensitivity]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-

| Paper
| [[2010Jiang2010_CTFCorrection]]
| Amplitude correction method
|-

| Paper
| [[2010Kasantsev_CTFCorrection]]
| Mathematical foundations of Kornberg and Jensen method
|-

| Paper
| [[2010Leong_CTFCorrection]]
| Correction for spatially variant CTF
|-

| Paper
| [[2011Glaeser_Coma]]
| The effect of coma at high-resolution
|-

| Paper
| [[2011Mariani_Tilted]]
| CTF simulation and correction of tilted specimens
|-

| Paper
| [[2011Sindelar_Wiener]]
| CTF correction using a modified version of Wiener filter
|-

| Paper
| [[2011Voortman_Tilted]]
| CTF correction for tilted specimen
|-

| Paper
| [[2012Voortman_VaryingCTF]]
| Correcting a spatially varying CTF
|-

| Paper
| [[2013Vargas_FastDef]]
| Fast defocus
|-

| Paper
| [[2014Penczek_CTER]]
| Estimation of the CTF errors
|-

| Paper
| [[2015Rohou_CTFFind4]]
| CTF Find 4
|-

| Paper
| [[2015Sheth_CTFquality]]
| Visualization and quality assessment of CTF
|-

| Paper
| [[2016Zhang_GCTF]]
| gCTF
|-

| Paper
| [[2018Su_GoCTF]]
| goCTF, CTF for tilted specimens
|-

| Paper
| [[2020Heimowitz_Aspire]]
| CTF determination in Aspire
|-

| Paper
| [[2020Zivanov_HighOrder]]
| Estimation of high-order aberrations
|-

| Paper
| [[2022Pant_ExitWave]]
| Estimation of the electron exit-wave
|-

| Paper
| [[2023Fernandez_Local]]
| Local defocus estimation
|-

| Paper
| [[2025Elferich_CTFFind5]]
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-

|}

=== Segmentation ===

{|

| Paper
| [[2006Baker_segmentation]]
| Segmentation of molecular subunits
|-

| Paper
| [[2010Pintilie_segger]]
| Segmentation of molecular subunits
|-

| Conference
| [[2017Nissenson_VolumeCut]]
| Segmentation of an EM volume using an atomic model
|-

| Paper
| [[2019Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate
|-

| Paper
| [[2020Beckers_FDR]]
| Segmentation of the protein using False Discovery Rate (GUI)
|-

| Paper
| [[2020Farkas_MemBlob]]
| Segmentation of membrane in membrane embedded proteins
|-

| Paper
| [[2020Terashi_MainMastSeg]]
| Segmentation of proteins into domains
|-

| Paper
| [[2022Ranno_Neural]]
| Neural representation of a map
|-

| Paper
| [[2021He_EMNUSS]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2024Sazzed_CryoSSESeg]]
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-

| Paper
| [[2025Cao_EMInfo]]
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-

|}

=== Fitting and docking ===

{|

| Paper
| [[1999Volkmann_Fitting]]
| Fitting in real space
|-

| Paper
| [[2001Baker_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2001Jones_Review]]
| Review of protein structure prediction
|-

| Paper
| [[2003Kovacs_FRM3D]]
| Fast Rotational Alignment of two EM maps
|-

| Paper
| [[2004Tama_NMA1]]
| Flexible fitting with Normal Modes (I)
|-

| Paper
| [[2004Tama_NMA2]]
| Flexible fitting with Normal Modes (II)
|-

| Paper
| [[2005Velazquez_Superfamilies]]
| Recognition of the superfamily folding in medium-high resolution volumes
|-

| Paper
| [[2007DeVries_Haddock]]
| Docking with Haddock 2.0
|-

| Paper
| [[2007Kleywegt_QualityControl]]
| Quality control and validation of fitting
|-

| Paper
| [[2008Orzechowski_Flexible]]
| Flexible fitting with biased molecular dynamics
|-

| Paper
| [[2008Rusu_Interpolation]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-

| Paper
| [[2012Biswas_Secondary]]
| Secondary structure determination in EM volumes
|-

| Paper
| [[2012Velazquez_Constraints]]
| Multicomponent fitting by using constraints from other information sources
|-

| Paper
| [[2013Chapman MS_Atomicmodeling]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-

| Paper
| [[2013Esquivel_Modelling]]
| Review on modelling (secondary structure, fitting, ...)
|-

| Paper
| [[2013Lopez_Imodfit]]
| Fitting based on vibrational analysis
|-

| Paper
| [[2013Nogales_3DEMLoupe]]
| Normal Mode Analysis of reconstructed volumes
|-

| Paper
| [[2014AlNasr_Secondary]]
| Identification of secondary structure elements in EM volumes
|-

| Paper
| [[2014Politis_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Rey_MassSpect]]
| Integration of mass spectroscopy information
|-

| Paper
| [[2014Villa_Review]]
| Review of atomic fitting into EM volumes
|-

| Paper
| [[2015Barad_EMRinger]]
| Validation of hybrid models
|-

| Paper
| [[2015Bettadapura_PF2Fit]]
| Fast rigid fitting of PDBs into EM maps
|-

| Paper
| [[2015Carrillo_CapsidMaps]]
| Analysis of virus capsids using Google Maps
|-

| Paper
| [[2015Hanson_Continuum]]
| Modelling assemblies with continuum mechanics
|-

| Paper
| [[2015Lopez_Review]]
| Review of structural modelling from EM data
|-

| Paper
| [[2015Schroeder_Hybrid]]
| Review on model building
|-

| Paper
| [[2015Tamo_Dynamics]]
| Dynamics in integrative modeling
|-

| Paper
| [[2015Sorzano_AtomsToVoxels]]
| Accurate conversion of an atomic model into a voxel density volume
|-

| Paper
| [[2016Joseph_Evolution]]
| Evolutionary constraints for the fitting of atomic models into density maps
|-

| Paper
| [[2016Joseph_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-

| Paper
| [[2016Murshudov_Refinement]]
| Refinement of atomic models in high-resolution EM reconstructions
|-

| Paper
| [[2016Segura_3Diana]]
| Validation of hybrid models
|-

| Paper
| [[2016Singharoy_MDFF]]
| Construction of hybrid models driven by EM density and molecular dynamics
|-

| Paper
| [[2016Wang_Rosetta]]
| Construction of hybrid models driven by EM density using Rosetta
|-

| Paper
| [[2017Chen_CoarseGraining]]
| Coarse graining of EM volumes
|-

| Paper
| [[2017Joseph_Metrics]]
| Metrics analysis for the comparison of structures
|-

| Paper
| [[2017Hryc_WeightedAtoms]]
| Construction of hybrid models by locally weighting the different atoms
|-

| Paper
| [[2017Matsumoto_Distribution]]
| Estimating the distribution of conformations of atomic models
|-

| Paper
| [[2017Michel_ContactPrediction]]
| Structure prediction by contact prediction
|-

| Paper
| [[2017Miyashita_EnsembleFitting]]
| Ensemble fitting using Molecular Dynamics
|-

| Paper
| [[2017Turk_ModelBuilding]]
| Tutorial on model building and protein visualization
|-

| Paper
| [[2017Wang_PartialCharges]]
| Appearance of partial charges in EM maps
|-

| Paper
| [[2017Wlodawer]]
| Comparison of X-ray and EM high resolution structures
|-

| Paper
| [[2018Cassidy_review]]
| Review of methods for hybrid modeling
|-

| Paper
| [[2018Chen_SudeChains]]
| A comparison of side chains between X-ray and EM maps
|-

| Paper
| [[2018Kawabata_Pseudoatoms]]
| Modelling the EM map with Gaussian pseudoatoms
|-

| Paper
| [[2018Kovacs_Medium]]
| Modelling of medium resolution EM maps
|-

| Paper
| [[2018Neumann_validation]]
| Validation of fitting, resolution assessment and quality of fit
|-

| Paper
| [[2018Terwilliger_map_to_model]]
| Phenix map_to_model, automatic modelling of EM volumes
|-

| Paper
| [[2018Wang_MD]]
| Constructing atomic models using molecular dynamics
|-

| Paper
| [[2018Xia_MVPENM]]
| Multiscale Normal Mode Analysis
|-

| Paper
| [[2018Yu_Atomic]]
| Constructing atomic models using existing tools
|-

| Paper
| [[2019Bonomi_Multiscale]]
| Bayesian multi-scale modelling
|-

| Paper
| [[2019Kidmose_Namdinator]]
| Namdinator: Flexible fitting with NAMD
|-

| Paper
| [[2019Kim_CryoFit]]
| CryoFit: flexible fitting in Phoenix
|-

| Paper
| [[2019Klaholz_Review]]
| Review of Phenix tools to modelling
|-

| Paper
| [[2019Subramaniya_DeepSSE]]
| Secondary structure prediction from maps using deep learning
|-

| Paper
| [[2019Zhang_CoarseGrained]]
| Coarse-graining of EM maps
|-

| Paper
| [[2020Costa_MDeNM]]
| Flexible fitting with molecular dynamics and normal modes
|-

| Paper
| [[2020Cragnolini_Tempy2]]
| TEMpy2 library for density-fitting and validation
|-

| Paper
| [[2020Dodd_ModelBuilding]]
| Model building possibilities, with special emphasis on flexible fitting
|-

| Paper
| [[2020Ho_CryoID]]
| Identification of proteins in structural proteomics from cryoEM volumes
|-

| Paper
| [[2020Hoh_Buccaneer]]
| Structure modelling with Buccaneer
|-

| Paper
| [[2020Joseph_comparison]]
| Comparison of map and model, or two maps
|-

| Paper
| [[2020Kim_Review]]
| Review of the options for atomic modelling
|-

| Paper
| [[2020Leelananda_Constraints]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-

| Paper
| [[2020Liebschner_Ceres]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-

| Paper
| [[2020Oroguchi]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-

| Paper
| [[2020Vant_Flexible]]
| Flexible fitting with molecular dynamics and neural network potentials
|-

| Paper
| [[2021Behkamal_Secondary]]
| Secondary structure from medium resolution maps
|-

| Paper
| [[2021Chojnowski_quality]]
| Quality of models automatically fitted with ARP/wARP
|-

| Paper
| [[2021Han_Vesper]]
| VESPER: global and local cryo-EM map alignment using local density vectors
|-

| Paper
| [[2021Lawson_Challenge]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-

| Paper
| [[2021Mori_Flexible]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-

| Paper
| [[2021Pfab_DeepTracer]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-

| Paper
| [[2021Saltzberg_IMP]]
| Using the Integrative Modeling Platform to model a cryoEM map
|-

| Paper
| [[2021Terwilliger_CryoID]]
| Identification of sequence in a CryoEM map from a set of candidates
|-

| Paper
| [[2021Titarenko_LocalCorr]]
| Performance improvement of local correlation for docking
|-

| Conference
| [[2021Vuillemot_NMA]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-

| Paper
| [[2022Antanasijevic_ab]]
| Sequence determination of antibodies bound to a map
|-

| Paper
| [[2022Behkamal_LPTD]]
| LPTD: Topology determination of CryoEM maps
|-

| Paper
| [[2022Bouvier_coevolution]]
| Atomic modelling exploiting residue coevolution
|-

| Paper
| [[2022Chojnowski_findMySeq]]
| Identify sequence in CryoEM map using Deep Learning
|-

| Paper
| [[2022Hryc_Pathwalking]]
| Atomic modelling with Pathwalking
|-

| Paper
| [[2022He_EMBuild]]
| Atomic modelling for complexes with EMbuild
|-

| Paper
| [[2022Krieger_Prody2]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-

| Paper
| [[2022Neijenhuis_Haddock]]
| Protein-protein interface refinement in complex maps with Haddock2.4
|-

| Paper
| [[2022Terwilliger_AlphaFold]]
| Iterative modelling with AlphaFold and experimental maps
|-

| Paper
| [[2022Urzhumtsev_Direct]]
| Calculation of the EM map from an atomic model
|-

| Paper
| [[2022Urzhumtsev_XrayEM]]
| Effect of the local resolution on the atomic modeling
|-

| Paper
| [[2022Vuillemot_NMMD]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-

| Paper
| [[2022Zhang_CRITASSER]]
| Atomic models of assemble protein structures with deep learning
|-

| Paper
| [[2023Blau_FittingML]]
| Maximum-likelihood fitting of atomic models in EM maps
|-

| Paper
| [[2023Chang_CryoFold]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-

| Paper
| [[2023Dai_CryoFEM]]
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-

| Paper
| [[2023Millan_LL]]
| Likelihood-based docking of models into cryo-EM maps
|-

| Paper
| [[2023Park_CSA]]
| Atomic model fitting using conformational space annealing
|-

| Paper
| [[2023Read_LL]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-

| Paper
| [[2023Reggiano_MEDIC]]
| Evaluation of atomic models using MEDIC
|-

| Paper
| [[2023Richardson_Overfitting]]
| Evaluation of overfitting errors in model building
|-

| Paper
| [[2023Sweeney_ChemEM]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-

| Paper
| [[2023Terashi_DAQrefine]]
| Atomic model refinement using AlphaFold2 and DAQ
|-

| Paper
| [[2023Terashi_DeepMainMast]]
| DeepMainMast: de novo modelling of CryoEM maps
|-

| Paper
| [[2023Terwilliger_AlphaFold]]
| Comparison of AlphaFold predictions with experimental maps and models
|-

| Paper
| [[2023Wang_CryoREAD]]
| CryoREAD: de novo modelling of nucleic acids
|-

| Paper
| [[2024Beton_Ensemble]]
| Ensemble fitting
|-

| Paper
| [[2024Chen_EModelX]]
| Atomic modelling de novo from cryoEM maps
|-

| Paper
| [[2024Dahmani_MDFF]]
| Accelerated MDFF flexible fitting
|-

| Paper
| [[2024Giri_CryoStruct]]
| CryoStruct: de novo modeling of cryoEM maps
|-

| Paper
| [[2024Gucwa_CMM]]
| CheckMyMetal: Metal analysis in CryoEM maps
|-

| Paper
| [[2024Jamali_Modelangelo]]
| ModelAngelo: Automated model building of cryoEM maps
|-

| Paper
| [[2024He_SHOT]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-

| Paper
| [[2024Hoff_EMMIVox]]
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-

| Paper
| [[2024Li_EMRNA]]
| EMRNA: de novo modeling of RNA structures
|-

| Paper
| [[2024Li_EM2NA]]
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-

| Paper
| [[2024Read_Interactive]]
| Interactive local docking
|-

| Paper
| [[2024Wang_DiffModeller]]
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-

| Paper
| [[2024Wankowicz_qFit]]
| Multiconformer modeling of cryoEM maps
|-

| Paper
| [[2024Wlodarski_cryoEnsemble]]
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-

| Paper
| [[2025Carr_Map2Seq]]
| Map-to-sequence workflow
|-

| Paper
| [[2025Chen_CryoEvoBuilding]]
| CryoEvoBuilding: Model building for intermediate resolution maps using evolutionary information
|-

| Paper
| [[2025Chen_GMMs]]
| Model building in heterogeneous maps
|-

| Paper
| [[2025Gyawali_Multimodal]]
| Model building exploiting AlphaFold
|-

| Paper
| [[2025Haloi_Ligand]]
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-

| Paper
| [[2025Hansel_Ligand]]
| Ligand docking using CryoEM maps to bias the process
|-

| Paper
| [[2025Karolczak_Ligand]]
| Ligand detection in CryoEM maps using deep learning
|-

| Paper
| [[2025Li_EMProt]]
| EMProt: atomic modelling of cryoEM maps using deep learning
|-

| Paper
| [[2025Luo_DiffFit]]
| DiffFit: Flexible fitting of map and atomic model
|-

| Paper
| [[2025Ma_DeepTracer]]
| DeepTracer-LowResEnhance: model building with low resolution
|-

| Paper
| [[2025Mallet_crAI]]
| crAI: detection of antibodies in cryoEM maps
|-

| Paper
| [[2025Matsuoka_ForceConstant]]
| Empirical determination of the force constant for flexible fitting
|-

| Paper
| [[2025Mondal_EMSequenceFinder]]
| EMSequenceFinder: identification of the aminoacid sequence from the cryoEM map
|-

| Paper
| [[2025Muenks_EmeraldID]]
| Emerald ID: Identification of small ligands in cryoEM maps
|-

| Paper
| [[2025Riahi_EMPOT]]
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-

| Paper
| [[2025Shub_Mic]]
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-

| Paper
| [[2025Su_CryoAtom]]
| CryoAtom: Model building using deep learning
|-

| Paper
| [[2025Wang_E3CryoFold]]
| E3CryoFold: model building in cryoEM maps
|-

| Paper
| [[2025Zhang_Emol]]
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-

| Paper
| [[2025Zhang_Benchmark]]
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-

| Paper
| [[2025Zheng_Disorder]]
| Exploration of disordered regions in CryoEM maps
|-

| Paper
| [[2026Behkamal_Secondary]]
| Identification of secondary structure topology from cryoEM map
|-

| Paper
| [[2026Chen_CryoEvoBuild]]
| CryoEvoBuild: Model building in maps with intermediate resolution
|-

| Paper
| [[2026Mulvaney_CASP16]]
| Relationship between local resolution, RMSF, pLDDT and SMOC in CASP16 CryoEM maps
|-

| Paper
| [[2026Savas_Distogram]]
| Use of AlphaFold distograms to interpret flexible regions in CryoEM maps
|-

|}

=== Books and reviews ===

{|

| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-

| Book
| [[1988Kak_Tomography]]
| General book on tomography
|-

| Paper
| [[2000Tao_Review]]
| Review of single particles
|-

| Paper
| [[2000VanHeel_Review]]
| Review of single particles
|-

| Paper
| [[2002Frank_Review]]
| Review of single particles
|-

| Paper
| [[2002Schmid_Review]]
| Review of single particles
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2004Subramaniam_Review]]
| Review of single particles
|-

| Paper
| [[2005Steven_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_book]]
| Book covering all aspects of electron microscopy of single particles
|-

| Paper
| [[2006Sorzano_Review]]
| Review of optimization problems in electron microscopy
|-

| Paper
| [[2007Leschziner_Review]]
| Review of 3D heterogeneity handling algorithms
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2008Mueller_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2008Taylor_Review]]
| Review of Electron microscopy
|-

| Paper
| [[2010DeRosier_Review]]
| Personal account of how 3DEM developed in the early days
|-

| Chapter
| [[2012Sorzano_Review]]
| Review of single particle analysis using Xmipp
|-

| Chapter
| [[2012Devaux_Protocol]]
| Protocols for performing single particle analysis
|-

| Paper
| [[2014Bai_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Carazo_Review]]
| Review of the reconstruction process
|-

| Paper
| [[2015Cheng_Review]]
| A primer to Single Particle Cryo-EM
|-

| Paper
| [[2015Cheng_Reviewb]]
| Single Particle Cryo-EM at crystallographic resolution
|-

| Paper
| [[2015Elmlund_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Henderson_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Nogales_Review]]
| Recent advances in cryo-EM
|-

| Paper
| [[2015Schroeder_Review]]
| Review of advances in the electron microscope
|-

| Paper
| [[2015VanDenBedem_Integrative]]
| Review of integrative structural biology
|-

| Paper
| [[2015Wu_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Carroni_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Egelman_CryoEM]]
| Review of advances in Cryo-EM
|-

| Paper
| [[2016Eisenstein_CryoEM]]
| News feature on the Method of the Year
|-

| Paper
| [[2016FernandezLeiro_Review]]
| Review of EM
|-

| Paper
| [[2016Glaeser_HowGood]]
| How good can cryo-EM become?
|-

| Paper
| [[2016Jonic_PseudoAtoms]]
| Review of the applications of the use of pseudoatoms in EM
|-

| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-

| Paper
| [[2016Jonic_Review]]
| A review of computational ways to handle heterogeneity
|-

| Paper
| [[2016Nogales_Review]]
| Review of advances in cryo-EM
|-

| Paper
| [[2016Subramaniam_Review]]
| Why cryo-EM is now suitable for crystallographic journals
|-

| Paper
| [[2016Vinothkumar_Review]]
| Historical review and current limitations
|-

| Report
| [[2017Brezinski_Nobel]]
| Scientific background on the Nobel Prize in Chemistry 2017
|-

| Paper
| [[2017Cheng_review]]
| Why CryoEM became so hot
|-

| Paper
| [[2017Danev_Review]]
| Review of the use of phase plates in EM
|-

| Paper
| [[2017Elmlund_Review]]
| Review of the main current difficulties of EM
|-

| Paper
| [[2017Frank_Review]]
| Historical review of EM
|-

| Paper
| [[2017Frank_TimeResolved]]
| Review of time-resolved of EM
|-

| Paper
| [[2017Jonic_Review]]
| Review of computational methods to analyze conformational variability
|-

| Paper
| [[2017Merino_DrugEM]]
| Applications of EM for drug design
|-

| Paper
| [[2017Rawson_Limitations]]
| Limitations of EM for drug design
|-

| Paper
| [[2017Sorzano_FourierProperties]]
| Review of statistical properties of resolution measures defined in Fourier space
|-

| Paper
| [[2017Sorzano_SurveyIterative]]
| Survey of iterative reconstruction methods for EM
|-

| Paper
| [[2018Bruggeman_Crowdsourcing]]
| Exploring crowdsourcing for EM image processing
|-

| Paper
| [[2018Cheng_Review]]
| Review of EM and future ahead
|-

| Paper
| [[2018Cossio_ML]]
| Review of Maximum Likelihood methods
|-

| Paper
| [[2018Grimes_Crystallography]]
| Review of X-ray crystallography and its relationship to EM
|-

| Paper
| [[2018Murata_Review]]
| Review of EM for structure dynamics
|-

| Paper
| [[2018Quentin_Biomedical]]
| Review of EM as a tool for biomedical research
|-

| Paper
| [[2018Scapin_DrugDiscovery]]
| Review of EM as a tool for drug discovery
|-

| Paper
| [[2018Vilas_ImageProcessing]]
| Review of the recent developments in image processing for single particle analysis
|-

| Paper
| [[2018vonLoeffelholz_VPP]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-

| Paper
| [[2018Eisenstein_DrugDesigners]]
| Drug designers embrace cryo-EM
|-

| Paper
| [[2019Benjin_Review]]
| Review of SPA
|-

| Paper
| [[2019Danev_Review]]
| Review of future directions
|-

| Paper
| [[2019Lyumkis_Review]]
| Challenges and reviews
|-

| Paper
| [[2019Sorzano_Review]]
| Review of continuous heterogeneity biophysics
|-

| Paper
| [[2019Urzhumtseva_Review]]
| Review of rotation conventions
|-

| Paper
| [[2020Abriata_Review]]
| Considerations of structure prediction and CryoEM
|-

| Paper
| [[2020Akbar_Review]]
| Review of membrane protein reconstructions
|-

| Paper
| [[2020Bendory_Review]]
| Review of image processing problems
|-

| Paper
| [[2020Dubach_Review]]
| Review of resolution in X-ray crystallography and CryoEM
|-

| TechReport
| [[2020Lai_Statistics]]
| Review of statistical properties of image alignment
|-

| Paper
| [[2020Hu_Quaternions]]
| Review of the use of quaternions to describe rotations
|-

| Paper
| [[2020McCafferty_Review]]
| Review of SPA and Mass Spectroscopy
|-

| Paper
| [[2020Seffernick_Hybrid]]
| Review of hybrid (computational and experimental) methods to get protein structure
|-

| Paper
| [[2020Nakane_Atomic]]
| Single-particle cryo-EM at atomic resolution
|-

| Paper
| [[2020Singer_Sigworth_Review]]
| Review of single particle analysis
|-

| Paper
| [[2020Vilas_Review]]
| Review of local resolution
|-

| Paper
| [[2020Wigge_Review]]
| Review of drug discovery with CryoEM
|-

| Paper
| [[2020Wu_Review]]
| Review of current limitations, with special emphasis on protein size
|-

| Paper
| [[2021Bai_Review]]
| Review of breakthroughs leading to atomic resolution
|-

| Paper
| [[2021DImprima_Review]]
| Review of sample preparation for single particle analysis
|-

| Paper
| [[2021Lander_Review]]
| Review of focused analysis in SPA
|-

| Paper
| [[2021Raimondi_Review]]
| General review of SPA
|-

| Paper
| [[2022Beton_Fitting]]
| Review of fitting in SPA
|-

| Paper
| [[2022Burley_PDB]]
| Review of cryoEM derived structures at PDB
|-

| Paper
| [[2022Caldraft_Tilt]]
| Review of applications of tilt pairs in SPA
|-

| Paper
| [[2022Donnat_GAN]]
| Review of Generative modelling with neural networks
|-

| Paper
| [[2022Guaita_Review]]
| Recent advances and current trends in cryo-electron microscopy
|-

| Paper
| [[2022Jones_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Namba_Review]]
| Review of the current state of SPA
|-

| Paper
| [[2022Ourmazd_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Palmer_Local]]
| Review of local methods in CryoEM
|-

| Paper
| [[2022Sorzano_1000]]
| CryoEM is the field of 1000+ methods
|-

| Paper
| [[2022Subramaniam_Comment]]
| Comment on the impact of AlphaFold and next challenges ahead
|-

| Paper
| [[2022Treder_DL]]
| Review of Deep Learning applications in CryoEM
|-

| Paper
| [[2022Vant_MD]]
| Review of Molecular Dynamics analysis of CryoEM maps
|-

| Paper
| [[2022Vilas_Emerging]]
| Review of image processing tools in SPA, including a comparison of deep learning and classical algorithms
|-

| Paper
| [[2023Amann_TimeResolved]]
| Review of time-resolved cryoEM
|-

| Paper
| [[2023Bai_Challenges]]
| Challenges and opportunities in structure determination
|-

| Paper
| [[2023Beton_Fitting]]
| Review of fitting tools in cryoEM
|-

| Paper
| [[2023DiIorio_AbInitio]]
| Review of ab initio reconstruction algorithms based on deep learning
|-

| Paper
| [[2023Liu_AWI]]
| Review of the Air-Water Interface
|-

| Paper
| [[2023Lucas_Structureome]]
| Review of the localization of proteins and complexes in their cellular context
|-

| Paper
| [[2023Miyashita_MD]]
| Review of the use of molecular dynamics in atomic modelling
|-

| Paper
| [[2023Si_DeNovo]]
| Review of the de-novo atomic modelling
|-

| Paper
| [[2023Tang_Conformational]]
| Review of conformational heterogeneity and probability distributions
|-

| Paper
| [[2023Toader_Heterogeneity]]
| Review of continuous heterogeneity
|-

| Paper
| [[2024Bock_MD]]
| Review of the joint use of Molecular Dynamics and CryoEM
|-

| Paper
| [[2024Bowlby_Flexible]]
| Review of continuous flexibility
|-

| Paper
| [[2024Cheng_Automated]]
| Review of automated acquisition
|-

| Paper
| [[2024Kimanius_Heterogeneity]]
| Review of heterogeneity analysis
|-

| Paper
| [[2024Lander_Validation]]
| Review of SPA validation
|-

| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-

| Paper
| [[2025Farheen_Modeling]]
| Review of structure modeling
|-

| Paper
| [[2025Kim_Review]]
| Review of SPA and protein-protein or protein-ligand docking
|-

| Paper
| [[2025Leone_Review]]
| Review of the integration of Molecular Dynamics with experimental techniques
|-

| Paper
| [[2025Patwardhan_Extending]]
| Perspective on technological developments leading to a wider application of cryoEM
|-

| Paper
| [[2025Wan_CryoETStandards]]
| Perspective on the need for CryoET standards
|-

| Paper
| [[2025Zhu_Quality]]
| Review of AI-based quality assessment of SPA maps
|-

| Paper
| [[2026Chrencik_SBDD]]
| Review of the role of Structural Biology in Pharma
|-

| Paper
| [[2026Gauvin_200kV]]
| Example of a 200kV CryoEM facility and discussion of its cost
|-

| Paper
| [[2026Kwon_Elastic]]
| Review of elastic and inelastic scattering
|-

| Paper
| [[2026Li_Atomic]]
| Review on deep learning algorithms for map sharpening and atomic modelling
|-

| Paper
| [[2026Lin_HDX]]
| Comment on the complementarity of HDX-MS studies for heterogeneous particles
|-

| Paper
| [[2026Subramaniam_Review]]
| Review on the general state of cryoEM
|-

| Paper
| [[2026Zhou_Atomic]]
| Review on strategies for atomic modelling of SPA maps
|-

|}

=== Software ===

{|

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[1999Lutdke_Eman]]
| Eman
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Baldwin_AngularTransformations]]
| The Transform Class in SPARX and EMAN2
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Grigorieff_Frealign]]
| Frealign
|-

| Paper
| [[2008Scheres_XmippProtocols]]
| Xmipp Protocols
|-

| Paper
| [[2008Shaikh_SpiderProtocols]]
| Spider Protocols
|-

| Paper
| [[2012Wriggers_SitusConventions]]
| Conventions and workflows in Situs
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2015Cianfrocco_Cloud]]
| Software execution in the cloud
|-

| Paper
| [[2015Cheng_MRC2014]]
| Extensions to MRC file format
|-

| Paper
| [[2013DeLaRosa_Scipion]]
| Scipion
|-

| Paper
| [[2016Scheres_Relion]]
| Tutorial on the use of Relion
|-

| Paper
| [[2016Grigorieff_Frealign]]
| Tutorial on the use of Frealign
|-

| Paper
| [[2017Moriya_Sphire]]
| Tutorial on the use of Sphire
|-

| Paper
| [[2018Bell_EMAN2]]
| New tools in EMAN2
|-

| Paper
| [[2018Cianfrocco_cloud]]
| CryoEM Cloud Tools
|-

| Paper
| [[2018Grant_cisTEM]]
| cisTEM
|-

| Paper
| [[2018McLeod_MRCZ]]
| MRC Compression format
|-

| Paper
| [[2018Zivanov_Relion3]]
| Relion 3
|-

| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-

| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-

| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-

| Paper
| [[2021Kimanius_Relion4]]
| Changes in Relion 4.0
|-

| Paper
| [[2021Maji_BlackBox]]
| Exploration of image processing concepts
|-

| Paper
| [[2021Sharov_Relion]]
| Use of Relion within Scipion
|-

| Paper
| [[2021Sorzano_Scipion]]
| Use of Scipion as a way to compare the results of multiple methods
|-

| Paper
| [[2021Strelak_Xmipp]]
| Advances in Xmipp
|-

| Paper
| [[2022DiIorio_Multiple]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-

| Paper
| [[2022Fluty_Precision]]
| Precision requirements and data compression
|-

| Paper
| [[2022Harastani_ContinuousFlex]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-

| Paper
| [[2022Warshamanage_EMDA]]
| A Python library for low-level computations such as local correlation
|-

| Paper
| [[2023Cheng_AutoEMage]]
| AutoEMage: a system for processing in streaming (SPA)
|-

| Paper
| [[2023Conesa_Scipion3]]
| Scipion3: A workflow engine for cryoEM
|-

| Paper
| [[2023Krieger_ScipionPrody]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-

| Paper
| [[2023Matinyan_TRPX]]
| TRPX compression format
|-

| Paper
| [[2023Short_MRC2020]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-

| Paper
| [[2024deLaRosa_EMHub]]
| A web-based Laboratory Information Management System for cryoEM facility
|-

| Paper
| [[2024Gonzalez_Dashboard]]
| A web-based dashboard for Relion
|-

| Paper
| [[2024Herre_Capsules]]
| SBGrid Capsules to execute programs in controlled environments
|-

| Paper
| [[2024Moriya_GoToCloud]]
| GoToCloud: SPA processing in the cloud
|-

| Paper
| [[2024Urzhumtseva_VUE]]
| VUE: Visualization of angular distributions
|-

| Paper
| [[2024Vuillemot_MDSPACE]]
| MDSpace and MDTomo to analyze continuous heterogeneity
|-

| Paper
| [[2024Wang_CryoSeek]]
| CryoSeek: protein identification with CryoEM, modelling and Mass spectroscopy
|-

| Paper
| [[2025Chen_CryoCRAB]]
| CryoCRAB: a large database of curated micrographs
|-

| Paper
| [[2025Costa_PERC]]
| PERC: a library to interact with CryoEM databases
|-

| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-

| Paper
| [[2025Khoshbin_Magellon]]
| Magellon: a software platform for CryoEM image processing
|-

| Paper
| [[2025Matinyan_TRPX]]
| TRPX v2: fast compression of raw files
|-

| Paper
| [[2026Marchan_Unattended]]
| Unattended workflow for SPA image processing in streaming
|-

|}

== Electron tomography ==

=== Data Collection ===

{|

| Paper
| [[2025Sharma_DataCollection]]
| Automation for cryo-electron tomography data collection
|-

|}

=== Image preprocessing ===

{|

| Paper
| [[2015Yan_thickness]]
| Determination of thickness, tilt and electron mean free path
|-

| Paper
| [[2018Wu_contrast]]
| Contrast enhancement to improve alignability
|-

|}

=== Image alignment ===

{|

| Paper
| [[1982Guckenberger_commonOrigin]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-

| Paper
| [[1992Lawrence_leastSquares]]
| Least squares solution of the alignment problem
|-

| Paper
| [[1995Penczek_dual]]
| Dual tilt alignment
|-

| Paper
| [[1996Owen_alignmentQuality]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-

| Paper
| [[1998Grimm_normalization]]
| Discussion of several gray level normalization methods for electron tomography
|-

| Paper
| [[2001Brandt_Automatic1]]
| Automatic alignment without fiducial markers
|-

| Paper
| [[2001Brandt_Automatic2]]
| Automatic alignment with fiducial markers
|-

| Paper
| [[2006Winkler_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2006Castano_alignment]]
| Alignment with non-perpendicularity
|-

| Paper
| [[2007Castano_alignment]]
| Fiducial-less alignment of cryo-sections
|-

| Paper
| [[2009Sorzano_alignment]]
| Marker-free alignment and refinement
|-

| Paper
| [[2010Cantele_dualAlignment]]
| Alignment of dual series
|-

| Paper
| [[2014Tomonaga_Automatic]]
| Automatic alignment of tilt series using the projection themselves
|-

| Paper
| [[2014Han_Automatic]]
| Automatic alignment of tilt series using SIFT features
|-

| Paper
| [[2015Han_Automatic]]
| Automatic alignment of tilt series using fiducials
|-

| Paper
| [[2017Mastronarde_Automatic]]
| Automatic alignment and reconstruction of tilt series in IMOD
|-

| Paper
| [[2018Fernadez_Beam]]
| Image alignment considering beam induced motion
|-

| Paper
| [[2018Han_Fast]]
| Automatic alignment using fiducial markers
|-

| Paper
| [[2019Fernandez_residual]]
| Alignment of tilt series using residual interpolation
|-

| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-

| Paper
| [[2020Sorzano_automatic]]
| Automatic alignment considering several geometrical distortions
|-

| Paper
| [[2021Han_LocalConstraints]]
| Automatic alignment considering local constraints
|-

| Paper
| [[2022Ganguly_SparseAlign]]
| Sparse Align: Automatic detection of markers and deformation estimation
|-

| Paper
| [[2022Zheng_Aretomo]]
| Automatic alignment based on projection matching
|-

| Paper
| [[2024Coray_Automated]]
| Automated fiducial-based tilt series alignment in Dynamo
|-

| Paper
| [[2024deIsidro_deep]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
|-

| Paper
| [[2024Hou_Marker]]
| Marker detection using wavelets
|-

| Paper
| [[2024Xu_MarkerAuto2]]
| MarkerAuto2: Tilt series alignment using fiducials
|-

| Paper
| [[2025deIsidro_Misalignment]]
| Tilt series misalignment detection
|-

| Paper
| [[2025Guo_Alignment]]
| Tilt series alignment with L1-norm optimization
|-

| Paper
| [[2026Xu_MarkerFree]]
| Fiducialess tilt series alignment
|-

|}

=== CTF estimation and restoration ===

{|

| Paper
| [[2003Winkler_CTF]]
| Focus gradient correction in electron tomography
|-

| Paper
| [[2006Fernandez_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-

| Paper
| [[2009Xiong_CTF]]
| CTF determination and correction for low dose tomographic tilt series
|-

| Paper
| [[2012Eibauer_CTF]]
| CTF determination and correction
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2017Turonova_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2017Kunz_3DCTF]]
| 3D CTF Correction
|-

| Paper
| [[2024Mastronarded_CTFPlotter]]
| CTF estimation with CTFPlotter
|-

| Paper
| [[2024Zhang_CTFMeasure]]
| Simultaneous CTF estimation for a whole tilt series
|-

| Paper
| [[2025Khavnekar_PSD]]
| Accurate PSD determination in tilt series
|-

|}

=== 3D reconstruction ===

{|

| Paper
| [[1972Gilbert_SIRT]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-

| Paper
| [[1973Herman_ART]]
| Algebraic Reconstruction Technique (ART)
|-

| Paper
| [[1984Andersen_SART]]
| Simultaneous Algebraic Reconstruction Technique (SART)
|-

| Paper
| [[1992Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[1997Marabini_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2002Fernandez_reconstruction]]
| Iterative reconstruction in electron tomography
|-

| Paper
| [[2007Radermacher_WBP]]
| Weighted Backprojection in electron tomography
|-

| Paper
| [[2008Fernandez_CARP]]
| Component Averaged Row Projections (CARP)
|-

| Paper
| [[2010Xu_Long]]
| Iterative reconstructions with long object correction and GPU implementation
|-

| Paper
| [[2012Herman General Superiorization]]
| Superiorization: an optimization heuristic for medical physics
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-

| Paper
| [[2013Goris_SIRT_TV_DART]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-

| Paper
| [[2013Briegel A_Challenge]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-

| Paper
| [[2013Messaoudi_EnergyFiltered]]
| 3D Reconstruction of Energy-Filtered TEM
|-

| Paper
| [[2014Paavolainen_Missing]]
| Compensation of the missing wedge
|-

| Paper
| [[2015Venkatakrishnan_MBIR]]
| 3D Reconstruction with priors
|-

| Paper
| [[2016Deng_ICON]]
| 3D Reconstruction with missing information restoration
|-

| Paper
| [[2016Guay_Compressed]]
| 3D Reconstruction using compressed sensing
|-

| Paper
| [[2016Turonova_Artifacts]]
| Artifacts observed during 3D reconstruction
|-

| Paper
| [[2019Yan_MBIR]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-

| Paper
| [[2020Sanchez_Hybrid]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2020Song_Tygress]]
| 3D reconstruction with a special acquisition and alignment scheme
|-

| Paper
| [[2021Fernandez_TomoAlign]]
| 3D reconstruction with sample motion and CTF correction
|-

| Paper
| [[2021Geng_Nudim]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-

| Paper
| [[2024vanVeen_Missing]]
| Missing wedge filling in cryoET
|-

| Paper
| [[2025Debarnot_IceTide]]
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-

|}

=== Noise reduction ===
{|

| Paper
| [[2001Frangakis_NAD]]
| Noise reduction with Nonlinear Anisotropic Diffusion
|-

| Paper
| [[2003Fernandez_AND]]
| Anisotropic nonlinear diffusion for electron tomography
|-

| Paper
| [[2003Jiang_Bilateral]]
| Bilateral denoising filter in electron microscopy
|-

| Paper
| [[2005Fernandez_AND]]
| Anisotropic nonlinear denoising in electron tomography
|-

| Paper
| [[2007Heide_median]]
| Iterative median filtering in electron tomography
|-

| Paper
| [[2007Fernandez_autAND]]
| Anisotropic nonlinear diffusion with automated parameter tuning
|-

| Paper
| [[2009Fernandez_Beltrami]]
| Nonlinear filtering based on Beltrami flow
|-

| Paper
| [[2010Bilbao_MeanShift]]
| Mean Shift Filtering
|-

| Paper
| [[2014Kovacik_wedgeArtefacts]]
| Removal of wedge artefacts
|-

| Paper
| [[2014Maiorca_beadArtefacts]]
| Removal of gold bead artefacts
|-

| Paper
| [[2018Trampert_Inpainting]]
| Removal of the missing wedge by inpainting
|-

| Paper
| [[2018Moreno_TomoEED]]
| Fast Anisotropic Diffusion
|-

| Paper
| [[2018Wu_Enhancement]]
| Enhancing the image contrast of electron tomography
|-

| Paper
| [[2022Liu_Isonet]]
| Isotropic reconstructions using deep learning
|-

| Paper
| [[2024vanBlerkom_GoldX]]
| GoldX: Gold bead removal
|-

| Paper
| [[2025Costa_CryoSamba]]
| CryoSamba: tomogram denoising
|-

|}

=== Segmentation ===

{|

| Paper
| [[2002Frangakis_Eigenanalysis]]
| Segmentation using eigenvector analysis.
|-

| Paper
| [[2002Volkmann_Watershed]]
| Segmentation using watershed transform.
|-

| Paper
| [[2003Bajaj_BoundarySegmentation]]
| Segmentation based on fast marching.
|-

| Paper
| [[2005Cyrklaff_Thresholding]]
| Segmentation using optimal thresholding.
|-

| Paper
| [[2007Lebbink_TemplateMatching]]
| Segmentation using template matching.
|-

| Paper
| [[2007Sandberg_OrientationFields]]
| Segmentation using orientation fields.
|-

| Paper
| [[2007Sandberg_SegmentationReview]]
| Review on segmentation in electron tomography.
|-

| Paper
| [[2008Garduno_FuzzySegmentation]]
| Segmentation using fuzzy set theory principles.
|-

| Paper
| [[2009Lebbink_TemplateMatching2]]
| Segmentation using template matching.
|-

| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| Parameter-Free Segmentation of Macromolecular Structures.
|-

| Paper
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Membrane segmentation.
|-

| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-

| Paper
| [[2017Ali_RAZA]]
| Automated segmentation of tomograms
|-

| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-

| Paper
| [[2017Tasel_ActiveContours]]
| Segmentation with active contours
|-

| Paper
| [[2017Xu_DeepLearning]]
| Finding proteins in tomograms using deep learning
|-

| Paper
| [[2018Zeng_DeepLearning]]
| Mining features in Electron Tomography by deep learning
|-

| Paper
| [[2020Salfer_PyCurv]]
| Curvature analysis of segmented tomograms
|-

| Paper
| [[2021Dimchev_filaments]]
| Segmentation of filaments in tomograms
|-

| Paper
| [[2022Frangakis_Curvature]]
| Use of mean curvature for segmentation and visualization of tomograms
|-

| Paper
| [[2022Lamm_MemBrain]]
| Membrane segmentation using deep learning
|-

| Paper
| [[2023Sazzed_Struwwel]]
| Detection and analysis of filament networks
|-

| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-

| Paper
| [[2024Gao_DomainFit]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
|-

| Paper
| [[2024Khosrozadeh_CryoVesNet]]
| CryoVesNet: Vesicle segmentation in cryo-electron tomograms
|-

| Paper
| [[2024Last_Ais]]
| Ais: Interactive segmentation of tomograms
|-

| Paper
| [[2024Siggel_ColabSeg]]
| Interactive membrane segmentation of tomograms
|-

| Paper
| [[2025Chen_GCTransNet]]
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-

| Paper
| [[2025Morales_Membranes]]
| Membrane segmentation with a neural network
|-

| Paper
| [[2025Schoennenbeck_CryoVIA]]
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-

|}

=== Resolution ===
{|

| Paper
| [[2005Cardone_Resolution]]
| Resolution criterion for electron tomography
|-

| Chapter
| [[2007Penczek_Resolution]]
| Review of resolution criteria for electron tomography
|-

| Paper
| [[2015Diebolder_ConicalFSC]]
| Conical Fourier Shell Correlation
|-

| Paper
| [[2020Vilas_Monotomo]]
| Resolution determination in tomograms
|-

|}

=== Subtomogram analysis ===

{|

| Paper
| [[2000Bohm_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2002Frangakis_Template]]
| Macromolecule finding by template matching
|-

| Paper
| [[2006Nickell_Review]]
| Review of macromolecule finding by template matching (Visual Proteomics)
|-

| Paper
| [[2007Best_Review]]
| Review of Localization of Protein Complexes by Pattern Recognition
|-

| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-

| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-

| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-

| Paper
| [[2008Schmid_Averaging]]
| Alignment and averaging of subtomograms
|-

| Paper
| [[2010Amat_Averaging]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-

| Paper
| [[2010Yu_PPCA]]
| Probabilistic PCA for volume classification
|-

| Paper
| [[2013Chen_Averaging]]
| Fast alignment of subtomograms using spherical harmonics
|-

| Paper
| [[2013Kuybeda_Averaging]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-

| Paper
| [[2013Shatsky_Averaging]]
| Alignment and averaging of subtomograms with constrained cross-correlation
|-

| Paper
| [[2013Yu_Projection]]
| Subtomogram averaging by aligning their projections
|-

| Paper
| [[2014Chen_Autofocus]]
| Subtomogram averaging and classification with special attention to differences
|-

| Paper
| [[2014Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2014Voortman_LimitingFactors]]
| Limiting factors of subtomogram averaging
|-

| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Subtomogram averaging with CTF correction using a Bayesian prior
|-

| Paper
| [[2015Yu_ReferenceBias]]
| Scoring the reference bias
|-

| Paper
| [[2016Bharat_Relion]]
| Subtomogram averaging with Relion
|-

| Paper
| [[2016Song_MatrixNorm]]
| Matrix norm minimization for tomographic reconstruction and alignment
|-

| Paper
| [[2017Castano_ParticlePicking]]
| Particle picking in tomograms for subtomogram averaging
|-

| Paper
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-

| Paper
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-

| Paper
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-

| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-

| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-

| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-

| Paper
| [[2020Zeng_GumNet]]
| GumNet: Subtomogram averaging using deep learning
|-

| Paper
| [[2021Cheng_Native]]
| 3D reconstruction only with 0-tilt images
|-

| Paper
| [[2021Du_Active]]
| Active learning to reduce the need of annotated samples
|-

| Paper
| [[2021Harastani_HEMNMA3D]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-

| Paper
| [[2021Lucas_Cistem]]
| Identification of particles in tomograms using Cistem
|-

| Paper
| [[2021Moebel_DeepFinder]]
| DeepFinder: Identification of particles in tomograms using neural networks
|-

| Paper
| [[2021Scaramuzza_Dynamo]]
| Subtomogram averaging workflow using Dynamo
|-

| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-

| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-

| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-

| Paper
| [[2022Bandyopadhyay_Adaptation]]
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-

| Paper
| [[2022Boehning_CompressedSensing]]
| Compressed sensing for subtomogram averaging
|-

| Paper
| [[2022Hao_Picking]]
| Detection of molecules in tomograms
|-

| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-

| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-

| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-

| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-

| Paper
| [[2023Balyschew_TomoBEAR]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-

| Paper
| [[2023Chaillet_Extensive]]
| Extensive angular sampling for picking in tomograms
|-

| Paper
| [[2023Cheng_GisSPA]]
| Detection of protein targets in 0-tilt images
|-

| Paper
| [[2023Genthe_PickYolo]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2023Rice_TomoTwin]]
| Subtomogram picking in tomograms
|-

| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-

| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-

| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-

| Paper
| [[2024Jin_Size]]
| Subtomogram picking based on size
|-

| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-

| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-

| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-

| Paper
| [[2024Rangan_CryoDRGNET]]
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-

| Paper
| [[2024Wan_StopGap]]
| StopGap: program to locate, align and classify subtomograms
|-

| Paper
| [[2024Wang_TomoNet]]
| Subtomogram picking in flexible lattices
|-

| Paper
| [[2025Chaillet_PytomMatchPick]]
| pytom-match-pick: particle picking in tomograms
|-

| Paper
| [[2025Shah_TomoCPT]]
| TomoCPT: particle picking in tomograms
|-

| Paper
| [[2025Yan_MPicker]]
| Membrane protein picking in electron tomograms
|-

| Paper
| [[2025Bartesaghi_StrategiesHet3D]]
| Strategies for studying discrete heterogeneity
|-

| Paper
| [[2026JCarrion_JDavis_insituHet3D]]
| Resolving structural heterogeneity in situ
|-

| Paper
| [[2026Schreiber_Turonova_TANGO]]
| Analysis and curation of particles
|-

| Paper
| [[2026Liu_nextPYP]]
| Conformational states with nextPYP
|-

|}

=== Single particle tomography ===

{|

| Paper
| [[2012Bartesaghi_Constrained]]
| 3D reconstruction by imposing geometrical constraints
|-

| Paper
| [[2012Zhang_IPET_FETR]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-

| Paper
| [[2015Galaz_SingleParticleTomography]]
| Set of tools for Single Particle Tomography in EMAN2
|-

| Paper
| [[2016Galaz_SingleParticleTomography]]
| Alignment algorithms and CTF correction
|-

|}

=== Missing-wedge correction ===

{|

| Paper
| [[2020Kovacs_Filaments]]
| Removal of missing wedge artifacts in filamentous tomograms
|-

| Paper
| [[2020Moebel_MCMC]]
| Missing wedge correction with Monte Carlo Markov Chains
|-

| Paper
| [[2020Zhai_LoTTor]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
|-

| Paper
| [[2023Zhang_REST]]
| Missing-wedge correction with neural networks
|-

| Paper
| [[2025Kiewisz_ProjectionSynthesis]]
| Projection synthesis of electron tomography data using neural networks
|-

|}

=== Molecular 3D dynamics ===

{|

| Paper
| [[2015Zhang_IPET]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
|-

| Paper
| [[2023Vuillemot_MDTOMO]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
|-

|}

=== Books and reviews ===

{|

| Paper
| [[2000Baumeister_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Koster_Review]]
| Review of electron tomography
|-

| Paper
| [[2003Sali_Review]]
| Review of electron tomography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2005Lucic_Review]]
| Review of electron tomography
|-

| Paper
| [[2006Fernandez_Review]]
| Review of electron microscopy
|-

| Book
| [[2006Frank_TomoBook]]
| Electron Tomography
|-

| Book
| [[2007McIntosh_Book]]
| Cellular Electron Microscopy
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

| Paper
| [[2008Fanelli_ImageFormation]]
| Review on the image formation model from the electron waves and open inverse-problems
|-

| Paper
| [[2008Fernandez_HPCReview]]
| High performance computing in electron cryomicroscopy
|-

| Paper
| [[2008Jonic_Review]]
| Comparison between electron tomography and single particles
|-

| Paper
| [[2012Kudryashev_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2013Briggs_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2016Beck_Review]]
| Review of molecular sociology
|-

| Paper
| [[2016Ercius_Review]]
| Electron tomography for hard and soft materials research
|-

| Paper
| [[2017Galaz_Review]]
| Review of single particle tomography
|-

| Paper
| [[2017Plitzko_Review]]
| Review of electron tomography, FRET and FIB milling
|-

| Paper
| [[2019Schur_Review]]
| Review of electron tomography and subtomogram averaging
|-

| Paper
| [[2021Frangakis_Review]]
| Review of tomogram denoising in electron tomography
|-

| Paper
| [[2022Forster_Review]]
| Review of subtomogram averaging
|-

| Paper
| [[2022Liedtke_Review]]
| Review of electron tomography in bacterial cell biology
|-

| Paper
| [[2022Liu_Review]]
| Review of beam image shift and subtomogram averaging
|-

| Paper
| [[2023Kim_Review]]
| Review of particle picking and volume segmentation
|-

| Paper
| [[2023Ochner_Review]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
|-

| Paper
| [[2023Zhao_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2023Watson_Review]]
| Review of computational methods for electron tomography
|-

| Paper
| [[2024Hutchings_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2024Schiotz_Review]]
| Review of in situ electron tomography
|-

| Paper
| [[2025Martinez_Review]]
| Review of template matching in electron tomography
|-

| Paper
| [[2025Wan_Review]]
| Review of sample preparation and data analysis for electron tomography
|-

|}

=== Software ===

{|

| Paper
| [[1996Kremer_IMOD]]
| IMOD
|-

| Paper
| [[1996Chen_Priism/IVE]]
| Priism/IVE
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2005Nickell_TOM]]
| TOM Toolbox
|-

| Paper
| [[2007Messaoudi_TomoJ]]
| TomoJ
|-

| Paper
| [[2008Heymann_BsoftTomo]]
| Bsoft
|-

| Paper
| [[2012Zhang IPET FETR]]
| IPET
|-

| Paper
| [[2015Ding_CaltechTomography]]
| Caltech tomography database
|-

| Paper
| [[2015Noble_AppionProtomo]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
|-

| Paper
| [[2015vanAarle_Astra]]
| ASTRA Toolbox
|-

| Paper
| [[2016Liu_FullMechTomo]]
| Fully mechanically controlled automated electron microscopic tomography
|-

| Paper
| [[2017Han_AuTom]]
| Software platform for Electron Tomography
|-

| Paper
| [[2017Wan_Simulator]]
| Electron Tomography Simulator
|-

| Paper
| [[2020Martinez-Sanchez_PySeg]]
| Template-free membrane proteins detection
|-

| Paper
| [[2021Burt_RWD]]
| Interoperability between Relion, Warp M, and Dynamo
|-

| Paper
| [[2022Jimenez_ScipionTomo]]
| Electron tomography within Scipion
|-

| Paper
| [[2022Martinez_PyOrg]]
| Point pattern analysis for coordinates in tomograms
|-

| Paper
| [[2022Ni_EmClarity]]
| Processing protocols with EmClarity
|-

| Paper
| [[2022Rodriguez_Mepsi]]
| Simulation of tomograms with membrane-embedded proteins
|-

| Paper
| [[2023Liu_NextPYP]]
| NextPYP: a software platform for cryoET
|-

| Paper
| [[2023Yee_Ot2Rec]]
| Ot2Rec: a software workflow for cryoET
|-

| Paper
| [[2024Burt_Relion5]]
| Subtomogram Analysis with RELION 5
|-

| Paper
| [[2024Comet_TomoLive]]
| TomoLive: Application for cryoET processing in streaming
|-

| Paper
| [[2024Gaifas_Blik]]
| Blik: Application for cryoET annotation and analysis
|-

| Paper
| [[2024Horstmann_PATo]]
| PATo: web application for cryoET processing in streaming
|-

| Paper
| [[2024Maurer_PyTME]]
| PyTME: Template matching for cryoET
|-

| Paper
| [[2024Martinez-Sanchez_PolNet]]
| PolNet: Simulating the Cellular Context
|-

| Paper
| [[2025Harar_FakET]]
| FakET: Simulation of electron tomography data using style transfer
|-

| Paper
| [[2025Zhan_AITom]]
| AITom: AI-guided CryoET Analysis Toolkit
|-

|}

== 2D Crystals ==

=== 2D Preprocessing ===

{|

| Paper
| [[1982Saxton_Averaging]]
| Radial Correlation Function
|-

| Paper
| [[1984Saxton_Distortions]]
| 3D Reconstruction of distorted crystals
|-

| Paper
| [[1986Henderson_Processing]]
| General 2D processing
|-

| Paper
| [[2000He_PhaseAlignment]]
| Phase consistency and Alignment
|-

| Paper
| [[2006Gil_Unbending]]
| Crystal unbending
|-

|}

=== Classification ===
{|

| Paper
| [[1988Frank_Classification]]
| MSA and classification in electron crystallography
|-

| Paper
| [[1996Fernandez_SOM]]
| Classification based on self organizing maps
|-

| Paper
| [[1998Sherman_MSA]]
| Classification based on MSA
|-

|}
=== 3D Reconstruction ===

{|

| Paper
| [[1985Wang_Solvent]]
| Solvent flattening
|-

| Paper
| [[1990Henderson_Processing]]
| General 3D processing
|-

| Paper
| [[2004Marabini_ART]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
|-

| Paper
| [[2018Biyani_Badlu]]
| Image processing for badly ordered crystals
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1998Walz_Review]]
| Review of 2D crystallography
|-

| Paper
| [[1999Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Ellis_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2001Glaeser_Review]]
| Review of 2D crystallography
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2006Fernandez_Review]]
| Review of single particles, electron tomography and crystallography
|-

| Paper
| [[2007Sorzano_Review]]
| Review of the image processing steps
|-

|}

=== Software ===

{|

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2007Gipson_2dx]]
| 2dx
|-

| Paper
| [[2007Heymann_Bsoft]]
| Bsoft
|-

| Paper
| [[2007Philippsen_IPLT]]
| IPLT
|-

|}

== 3D Crystals - MicroED ==

=== Sample Preparation ===
{|
| Paper
| [[2016Shi_Preparation]]
| Sample Preparation
|-

| Paper
| [[2024Gillman_Cone]]
| Eliminating the missing cone

|}

=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation

|}
=== Data Processing ===
{|

| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| Fragment-based phase extension
|-

| Paper
| [[2015Hattne_Processing]]
| Data Processing
|-

| Paper
| [[2016Hattne_Correction]]
| Image correction
|-

| Paper
| [[2026Thibodeaux_MR]]
| Fast molecular replacement algorithm for MicroED

|}

=== Software ===
{|

| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}

=== Books and Reviews ===
{|
| Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Liu_Review ]]
| Review of MicroED
|-

| Paper
| [[2016Rodriguez_Review ]]
| Review of MicroED
|-

|}

== Helical particles ==

=== Filament picking ===

{|

| Paper
| [[2021Thurber_Automated]]
| Automated picking of filaments
|-

| Paper
| [[2023Li_Classification]]
| Classification of filament segments using language models
|-

| Paper
| [[2025Peng_DiamTR]]
| DiamTR: Classification of filaments by diameter
|-

|}

=== Filament corrections ===

{|

| Paper
| [[1986Egelman_Curved]]
| Algorithm for correcting curved filaments
|-

| Paper
| [[1988Bluemke_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2006Wang_Pitch]]
| Algorithm for correcting filaments with different helical pitches
|-

| Paper
| [[2016Yang_Flexible]]
| Algorithm for correcting filaments with flexible subunits
|-

| Paper
| [[2019Ohashi_SoftBody]]
| Algorithm for correcting filaments with flexible helices
|-

|}

=== Reconstruction ===

{|

| Paper
| [[1952Cochran_Fourier]]
| Fourier Bessel transform of filamentous structures
|-

| Paper
| [[1958Klug_Fourier]]
| Fourier Bessel decomposition of the projection images
|-

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1988Stewart_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2005Wang_Iterative]]
| Iterative Fourier-Bessel algorithm
|-

| Paper
| [[2007Egelman_Iterative]]
| Iterative real-space algorithm
|-

| Paper
| [[2010Egelman_Pitfalls]]
| Pitfalls in helical reconstruction
|-

| Paper
| [[2013Desfosses_Spring]]
| Helical processing with Spring
|-

| Paper
| [[2015Zhang_seam]]
| Workflow for the detection of the lattice seam
|-

| Paper
| [[2016Rohou_Frealix]]
| Helical processing with Frealix
|-

| Paper
| [[2017_He]]
| Helical processing with Relion
|-

| Paper
| [[2019_Pothula]]
| 3D Classification through 2D analysis
|-

| Paper
| [[2025_Huang]]
| Helical parameter estimation by cylinder unrolling
|-

| Paper
| [[2025Li_Helicon]]
| Helicon: Helical parameter determination and 3D reconstruction from one image
|-

|}

=== Validation ===

{|

| Paper
| [[2014Egelman_ambiguity]]
| How to detect incorrect models
|-

| Paper
| [[2025Li_validation]]
| Validation of the helical symmetry parameters in EMDB
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1970DeRosier_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[1992Morgan_Rec]]
| Image processing steps towards 3D reconstruction
|-

| Paper
| [[2004Henderson_Review]]
| Review of electron microscopy
|-

| Paper
| [[2015Sachse_Review]]
| Review of the image processing steps in helical particles
|-

| Paper
| [[2021Egelman_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Wang_Review]]
| Review of reconstruction problems in helical structures
|-

| Paper
| [[2022Kreutzberger_Review]]
| Review of helical reconstruction
|-

|}

=== Software ===

{|

| Paper
| [[1996Carragher_Phoelix]]
| Phoelix
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Owen_Brandeis]]
| Brandeis
|-

|}

== Icosahedral particles ==

=== Reconstruction ===

{|

| Paper
| [[1970Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1971Crowther_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1996Fuller_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[1997Thuman_Rec]]
| Reconstruction of icosahedral viruses in Fourier space
|-

| Paper
| [[2019Goetschius_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

| Paper
| [[2025Chen_Asymmetric]]
| Approaches to reconstruct asymmetric features in viruses
|-

|}

=== Classification ===

{|

| Paper
| [[2005Scheres_Virus]]
| Classification of virus capsids in real space
|-

|}

=== Books and reviews ===

{|

| Paper
| [[1999Baker_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[1999Conway_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2000Thuman_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Lee_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2003Navaza_Review]]
| Review of reconstruction of icosahedral viruses
|-

| Paper
| [[2006Grunewald_Review]]
| Review of reconstruction of icosahedral viruses
|-

|}

=== Software ===

{|

| Paper
| [[1996Baker_EMPFT]]
| EMPFT
|-

| Paper
| [[1996Crowther_MRC]]
| MRC
|-

| Paper
| [[1996Frank_Spider]]
| Spider
|-

| Paper
| [[1996VanHeel_Imagic]]
| Imagic
|-

| Paper
| [[2004Sorzano_Xmipp]]
| Xmipp
|-

| Paper
| [[2013DeLaRosa_Xmipp30]]
| Xmipp 3.0
|-

| Paper
| [[2013Morin_Sliz SBGrid]]
| SBGrid presentation for eLife
|-

|}

== Single molecule 3D structure (non-averaged) ==

=== Variety analysis methods ===

{|

| Paper
| [[2024Liu_RNA]]
| Variety of RNA tertiary structures
|-

| Paper
| [[2024Zhang_Nucleosome]]
| Dynamics of nucleosome arrays
|-

| Paper
| [[2022Zhang_NucleosomeTrasition]]
| Aggregation of nucleosome arrays during phase transition
|-

| Paper
| [[2018Lei_DNABennet]]
| Flexibility of DNA origami Bennett linkages
|-

| Paper
| [[2016Zhang_DNANG]]
| Flexibility of DNA-nanogold complex
|-

| Paper
| [[2015Zhang_IgG1]]
| Dynamics of IgG1 antibodies
|-

|}

=== Process methods ===

{|

| Paper
| [[2012Zhang_IPET]]
| Forcused electron tomography reconstration (FETR) method
|-

| Paper
| [[2016Liu_AutoET]]
| Fully Mechanically Controlled Automated Electron Microscopic Tomography
|-

| Paper
| [[2018Wu_Contrast]]
| An Algorithm for Enhancing the Image Contrast of Electron Tomography
|-

| Paper
| [[2020Zhai_LoTToR]]
| Missing-wedge correction for the low-tilt tomographic 3D reconstruction of a single molecule
|-

|}

=== Reviews ===

{|

| Paper
| [[2022Han_Radiation]]
| Cryo-ET related radiation-damage parameters for single molecule 3D structure determination
|-

|}

== Liquid-cell TEM / in-situ TEM ==

{|

| Paper
| [[2020Ren_LTEM]]
| Real-time dynamic imaging of sample in liquid phase
|-

| Paper
| [[2023Kong_ViralEntry]]
| Molecular imaging of protein, virus and cell samples at room temperature
|-

|}

== Databases ==

{|

| Paper
| [[2003Boutselakis_EMSD]]
| EMSD database
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2005Heymann_Conventions]]
| Conventions for software interoperability
|-

| Paper
| [[2011Kim_CDDB]]
| Conformational Dynamics Data Bank
|-

| Paper
| [[2011Lawson_EMDB]]
| Electron Microscopy Data Bank
|-

| Paper
| [[2013Ison_EDAM]]
| EDAM, an ontology of bioinformatics operations
|-

| Paper
| [[2016Iudin_EMPIAR]]
| EMPIAR raw data database
|-

| Paper
| [[2016Patwhardan_EMDB]]
| EMDB, PDB, ...
|-

| Paper
| [[2017Gore_Validation]]
| Validations of PDB submissions
|-

| Paper
| [[2017Patwhardan_Trends]]
| Trends at EMDB
|-

| Paper
| [[2017Shao_PDBQuality]]
| Quality metrics in PDB
|-

| Paper
| [[2018Tawari_search]]
| Search of 3D structures in a database using 2D experimental images
|-

| Paper
| [[2018wwwPDB_PDB]]
| Review of PDB advances
|-

| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-

| Paper
| [[2022Wang_EMDB]]
| Validation analysis of EMDB entries
|-

| Paper
| [[2022Westbrook_mmCIF]]
| PDBx/mmCIF ecosystem
|-

| Paper
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
|-

| Paper
| [[2024Ermel_DataPortal]]
| CryoET Data Portal
|-

| Paper
| [[2024Vallat_IHMCIF]]
| IHMCIF extension of mmCIF for integrative modelling
|-

| Paper
| [[2024wwPDB_EMDB]]
| Review of EMDB
|-

| Paper
| [[2025Giri_Sharpening]]
| A labeled dataset for map enhancement
|-

| Blog
| http://www.mybiosoftware.com
| Huge list of bioinformatics programs (many of them structural bioinformatics)
|-

|}

== Relationship to other structural information sources ==

{|

| Paper
| [[2000Engel_AFM]]
| Review of Atomic Force Microscopy
|-

| Paper
| [[2001Dimmeler_AFM]]
| Constraints from Atomic Force Microscopy
|-

| Paper
| [[2003Mobus_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_EnergyLoss]]
| Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2004Leapman_Review]]
| Review on correlative microscopy
|-

| Paper
| [[2005Boudier_EFTETJ]]
| Software for Chemical mapping by energy loss electron tomography
|-

| Paper
| [[2005Vestergaard_SAXS]]
| Example of comparison of 3DEM and Small-angle X-ray scattering
|-

| Paper
| [[2007Hamada_SAXS]]
| Constraints from Small-angle X-ray scattering
|-

| Paper
| [[2013Xu_FRET]]
| EM+FRET
|-

| Paper
| [[2017Kim_SAXS]]
| Compatibility of EM experimental images and SAXS curves
|-

| Paper
| [[2018Ando_Correlative]]
| Review of correlative microscopy techniques
|-

| Paper
| [[2018Sieben_Multicolor]]
| Correlative microscopy with superresolution optical images
|-

| Paper
| [[2019Huber_EDXS_HAADF]]
| Combined reconstruction using EDXS and HAADF data
|-

| Paper
| [[2019Jimenez_SAXS]]
| Selection of EM initial volumes by SAXS curves
|-

| Paper
| [[2022Graziadei_CrossLinking]]
| Review on the use of crosslinking mass spectrometry in CryoEM
|-

| Paper
| [[2022Klumpe_FIB]]
| A modular platform for automated cryo-FIB workflows
|-

|}

== X-ray tomography ==

{|

| Paper
| [[2012Oton_ImageFormation]]
| Image formation model in X-ray cell microscopy
|-

|}

== Mathematical tools necessary ==

== People developing methods ==
Please, add yourself to this list (due to privacy reasons, please, do not add anyone else to the list without his/her explicit consent). Sort by first name alphabetical order.

[http://i2pc.es/coss Carlos Oscar S. Sorzano]: CSIC, Madrid, Spain 

[http://www.curie.fr/recherche/themes/detail_equipe.cfm/lang/_fr/id_equipe/241.htm Cédric Messaoudi]: Institute Curie, Paris, France 

[http://scholar.google.es/citations?user=_jNYGScAAAAJ&hl=en Javier Vargas]:: CSIC, Madrid, Spain 

[http://scholar.google.es/citations?hl=es&user=g2ZJPIYAAAAJ Joaquín Otón]: CSIC, Madrid, Spain 

[José Román Bilbao-Castro]: UAL, Almería, Spain; CSIC, Madrid, Spain 

[José Miguel de la Rosa Trevín]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[http://cryoem.janelia.org Tamir Gonen]: Howard Hughes Medical Institute, Ashburn, VA, USA 

[https://scholar.google.com/citations?user=KcUL5tsAAAAJ Tomas Majtner]: Biocomputing Unit CNB-CSIC, Madrid, Spain 

[Vahid Abrishami]: CSIC, Madrid, Spain 

[https://rengroup.lbl.gov/ Gang (Gary) Ren]: The Molecular Foundry, LBNL, USA 

== 3DEM sites ==
* [http://3dem.ucsd.edu/ 3DEM web site]

* [http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy Software tools for molecular microscopy]

* [http://www.emdatabank.org/ The Electron Microscopy Data Bank (EMDB)]

* [http://ccdb.ucsd.edu The Cell Centered Database (CCDB)]

* [http://conventions.cnb.csic.es The geometrical software conventions web page ]

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