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== Electron microscopy images ==
== 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]


=== Online courses and Learning material ===
=== Online courses and Learning material ===
Line 112: Line 118:


[https://www.youtube.com/@Cryo-EMAcademy Cryo-EMAcademy YouTube]
[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 ===
=== Image formation ===
Line 196: Line 204:
| [[2004Sorzano_Normalization]]
| [[2004Sorzano_Normalization]]
| Background noise is Gaussian
| Background noise is Gaussian
|-
| Paper
| [[2008Downing_Twin]]
| Theoretical analysis of the CTF correction algorithms
|-  
|-  


Line 471: Line 484:
| [[2021Glaeser_Fading]]
| [[2021Glaeser_Fading]]
| Defocus-dependent Thon-ring fading
| Defocus-dependent Thon-ring fading
|-
| Paper
| [[2021Singer_Wilson]]
| Detailed analysis of Wilson statistics
|-  
|-  


Line 476: Line 494:
| [[2021Wieferig_Devitrification]]
| [[2021Wieferig_Devitrification]]
| Devitrification reduces beam-induced movement in cryo-EM
| Devitrification reduces beam-induced movement in cryo-EM
|-
| Paper
| [[2022Bharadwaj_Scattering]]
| Electron scattering properties and their use for map sharpening
|-  
|-  


Line 516: Line 539:
| [[2023Schreiber_charge]]
| [[2023Schreiber_charge]]
| Time dynamics of charge buildup
| 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
|-  
|-  


Line 521: Line 554:
| [[2024Dickerson_magnification]]
| [[2024Dickerson_magnification]]
| Accurate determination of magnification using gold
| Accurate determination of magnification using gold
|-
| Paper
| [[2024Joosten_Roodmus]]
| Simulation of micrographs of heterogeneous macromolecules
|-  
|-  


Line 526: Line 564:
| [[2024Parkhurst_IceSimulation]]
| [[2024Parkhurst_IceSimulation]]
| Projections of amorphous ice simulation simulated with Gaussian Random Fields
| 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
| [[2025Wu_ZeroLossCCCorrected]]
| Imaging with chromatic aberration correction and zero loss electrons
|-  
|-  


Line 973: Line 1,026:
| [[2023Basanta_Graphene]]
| [[2023Basanta_Graphene]]
| Fabrication of Monolayer Graphene-Coated Grids  
| Fabrication of Monolayer Graphene-Coated Grids  
|-
| Paper
| [[2023Grassetti_Graphene]]
| Improving graphane monolayer sample preparation
|-
|-


Line 983: Line 1,041:
| [[2023Liu_AirWater]]
| [[2023Liu_AirWater]]
| Review on sample preparation techniques to deal with the air-water interface
| Review on sample preparation techniques to deal with the air-water interface
|-
| Paper
| [[2023Langeberg_RNAScaffold]]
| RNA scaffolds for small proteins
|-
|-


Line 1,006: Line 1,069:


| Paper
| Paper
| [[2024Yadav_Orientation]]
| [[2024Esfahani_SPOTRASTR]]
| Experimental factors affecting orientation distribution
| SPOT-RASTR: A sample preparation technique that overcomes preferred orientations
|-
|-


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


=== Automated data collection ===
| Paper
| [[2024Bhattacharjee_TimeResolved]]
| Time-resolved cryoEM with a microfluidic device
|-


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


| Paper
| Paper
| [[1992Dierksen_Automatic]]
| [[2024Henderikx_Vitrojet]]
| Automated data collection
| 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
| [[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
|-
 
|}
 
=== Automated data collection ===
 
{|
 
| Paper
| [[1992Dierksen_Automatic]]
| Automated data collection
|-  


| Paper
| Paper
Line 1,164: Line 1,302:
| [[2020Li_Workflow]]
| [[2020Li_Workflow]]
| Workflow for automatic reconstruction
| Workflow for automatic reconstruction
|-
| Paper
| [[2020Maruthi_Automatic]]
| Evaluation of MicAssess and CryoAssess
|-  
|-  


Line 1,205: Line 1,348:
| [[2021Chreifi_FISE]]
| [[2021Chreifi_FISE]]
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
| Rapid tilt-series method for cryo-electron tomography: Characterizing stage behavior during FISE acquisition
|-
| Paper
| [[2021Danev_Eval]]
| Evaluation of different automatic acquisition schemes
|-  
|-  


Line 1,317: Line 1,465:
|-  
|-  


|}
| 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
|-
 
|}


== Single particles ==
== Single particles ==
Line 1,509: Line 1,687:
| [[2022Huang_DenoisingAndPicking]]
| [[2022Huang_DenoisingAndPicking]]
| Simultaneous denoising and picking with deep learning
| Simultaneous denoising and picking with deep learning
|-
| Paper
| [[2022Kreymer_MTD]]
| Expectation-Maximization approach to particle picking
|-  
|-  


Line 1,525: Line 1,708:
| A public database for particle picking
| A public database for particle picking
|-  
|-  
|}
=== 2D Preprocessing ===
{|


| Paper
| Paper
| [[1978Carrascosa_matching]]
| [[2023Lucas_Baited]]
| Gray values matching by linear transformations
| Baited reconstruction with 2D template matching
|-  
|-  


| Paper
| Paper
| [[2003Rosenthal_DPR]]
| [[2024Anuk_Auction]]
| Contrast enhancement through DPR
| Particle picking using combinatorial auction
|-  
|-  


| Paper
| Paper
| [[2004Sorzano_Normalization]]
| [[2024Cameron_REPIC]]
| Normalization procedures and their statistical properties.
| Consensus 2D particle picking using graphs
|-  
|-  


| Paper
| Paper
| [[2006Sorzano_Denoising]]
| [[2024Fang_Swin]]
| Strong denoising in wavelet space
| SwinCryoEM: particle picking
|-  
|-  


| Conference
| Paper
| [[2009Sorzano_Downsampling]]
| [[2024Gyawali_CryoSegNet]]
| Differences between the different downsampling schemes
| CryoSegNet: particle picking
|-  
|-  


| Paper
| Paper
| [[2012Brilot_Movies]]
| [[2024Huang_Joint]]
| Alignment of beam induced motion in direct detectors
| Joint denoising and picking
|-  
|-  


| Paper
| Paper
| [[2012Campbell_Movies]]
| [[2025Chung_CRISP]]
| Alignment of beam induced motion in direct detectors
| Particle picking with deep learning and Conditional Random Field layers
|-  
|-  


| Paper
| Paper
| [[2012Zhao_Denoising]]
| [[2025Dhakal_Benchmark]]
| Denoising using an invariant Fourier-Bessel eigenspace
| Benchmark of particle picking with deep learning
|-  
|-  


| Paper
| Paper
| [[2013Norousi_Screening]]
| [[2025Neiterman_Frames]]
| Screening particles to identify outliers
| Particle picking at the level of frames
|-  
|-  


| Paper
| Paper
| [[2013Bai_ElectronCounting]]
| [[2025Ni_GTPick]]
| Electron counting and beam induced motion correction
| GTPick: Particle picking with deep learning
|-  
|-  


| Paper
| Paper
| [[2013Li_ElectronCounting]]
| [[2025Zamanos_CryoEMMAE]]
| Electron counting and beam induced motion correction
| Fully unsupervised particle picking using neural networks
|-  
|-  


| Paper
| Paper
| [[2013Shigematsu_Movies]]
| [[2025Zhang_2DTMpValue]]
| Drift correction for movies considering dark field
| p-value of the 2D template matching SNR and z-scores
|-  
|-  
|}
=== 2D Preprocessing ===
{|


| Paper
| Paper
| [[2013Vargas_ParticleQuality]]
| [[1978Carrascosa_matching]]
| Automatic determination of particle quality
| Gray values matching by linear transformations
|-  
|-  


| Paper
| Paper
| [[2014Scheres_Movies]]
| [[2003Rosenthal_DPR]]
| Beam induced motion correction
| Contrast enhancement through DPR
|-  
|-  


| Paper
| Paper
| [[2015Abrishami_Movies]]
| [[2004Sorzano_Normalization]]
| Alignment of direct detection device micrographs
| Normalization procedures and their statistical properties.
|-  
|-  


| Paper
| Paper
| [[2015Grant_Anisotropic]]
| [[2006Sorzano_Denoising]]
| Automatic estimation and correction of anisotropic magnification
| Strong denoising in wavelet space
|-
 
| Conference
| [[2009Sorzano_Downsampling]]
| Differences between the different downsampling schemes
|-  
|-  


| Paper
| Paper
| [[2015Grant_OptimalExposure]]
| [[2012Brilot_Movies]]
| Filter movies according to the radiation damage
| Alignment of beam induced motion in direct detectors
|-  
|-  


| Paper
| Paper
| [[2015Rubinstein_Alignment]]
| [[2012Campbell_Movies]]
| Frame alignment at the level of particle
| Alignment of beam induced motion in direct detectors
|-  
|-  


| Paper
| Paper
| [[2015Spear_DoseCompensation]]
| [[2012Zhao_Denoising]]
| Effect of dose compensation on resolution
| Denoising using an invariant Fourier-Bessel eigenspace
|-  
|-  


| Paper
| Paper
| [[2015Zhao_AnisotropicMagnification]]
| [[2013Norousi_Screening]]
| Correction of anisotropic magnification
| Screening particles to identify outliers
|-  
|-  


| Conference
| Paper
| [[2016Bajic_Denoising]]
| [[2013Bai_ElectronCounting]]
| Denoising and deconvolution of micrographs
| Electron counting and beam induced motion correction
|-  
|-  


| Paper
| Paper
| [[2016Jensen_RemovalVesicles]]
| [[2013Li_ElectronCounting]]
| Removal of vesicles in membrane proteins
| Electron counting and beam induced motion correction
|-  
|-  


| Paper
| Paper
| [[2016Bhamre_Denoising]]
| [[2013Shigematsu_Movies]]
| Denoising by 2D covariance estimation
| Drift correction for movies considering dark field
|-  
|-  


| Paper
| Paper
| [[2017Berndsen_EMPH]]
| [[2013Vargas_ParticleQuality]]
| Automated hole masking algorithm
| Automatic determination of particle quality
|-  
|-  


| Paper
| Paper
| [[2017McLeod_Zorro]]
| [[2014Scheres_Movies]]
| Movie alignment by Zorro
| Beam induced motion correction
|-  
|-  


| Paper
| Paper
| [[2017Zheng_MotionCorr2]]
| [[2015Abrishami_Movies]]
| Movie alignment by MotionCorr2
| Alignment of direct detection device micrographs
|-  
|-  


| Paper
| Paper
| [[2018Ouyang_Denoising]]
| [[2015Grant_Anisotropic]]
| Denoising based on geodesic distance
| Automatic estimation and correction of anisotropic magnification
|-  
|-  


| Paper
| Paper
| [[2018Wu_ContrastEnhancement]]
| [[2015Grant_OptimalExposure]]
| Contrast enhancement
| Filter movies according to the radiation damage
|-  
|-  


| Paper
| Paper
| [[2019Zivanov_BayesianBIM]]
| [[2015Rubinstein_Alignment]]
| Bayesian correction of beam induced movement
| Frame alignment at the level of particle
|-  
|-  


| Paper
| Paper
| [[2020Bepler_TopazDenoise]]
| [[2015Spear_DoseCompensation]]
| Preprocessing of micrographs for better picking
| Effect of dose compensation on resolution
|-  
|-  


| Paper
| Paper
| [[2020Chung_2SDR]]
| [[2015Zhao_AnisotropicMagnification]]
| PCA to denoise particles
| Correction of anisotropic magnification
|-
 
| Paper
| [[2020Chung_Prepro]]
| Preprocessing of particles for better alignment
|-  
|-  


| Conference
| Conference
| [[2020Huang_SuperResolution]]
| [[2016Bajic_Denoising]]
| Deep learning superresolution combination of frames
| Denoising and deconvolution of micrographs
|-  
|-  


| Paper
| Paper
| [[2020Palovcak_noise2noise]]
| [[2016Jensen_RemovalVesicles]]
| Noise2noise denoising of micrographs
| Removal of vesicles in membrane proteins
|-  
|-  


| Paper
| Paper
| [[2020Strelak_FlexAlign]]
| [[2016Bhamre_Denoising]]
| Continuous deformation model for aligning movie frames
| Denoising by 2D covariance estimation
|-  
|-  


| Conference
| Paper
| [[2021Fan_Denoising]]
| [[2017Berndsen_EMPH]]
| Particle denoising using vector diffusion maps
| Automated hole masking algorithm
|-  
|-  


| Paper
| Paper
| [[2022Heymann_ProgressiveSSNR]]
| [[2017McLeod_Zorro]]
| Progressive SSNR to assess quality and radiation damage
| Movie alignment by Zorro
|-  
|-  


| Paper
| Paper
| [[2022Shi_Denoising]]
| [[2017Zheng_MotionCorr2]]
| Contrast estimation and denoising in SPA
| Movie alignment by MotionCorr2
|-  
|-  


| Paper
| Paper
| [[2023Huang_ZSSR]]
| [[2018Ouyang_Denoising]]
| Multiple image super-resolution, upsampling with deep learning
| Denoising based on geodesic distance
|-  
|-  


| Paper
| Paper
| [[2023Marshall_PCA]]
| [[2018Wu_ContrastEnhancement]]
| Fast PCA on single particle images
| Contrast enhancement
|-  
|-  


| Paper
| Paper
| [[2023Sharon_Enhancement]]
| [[2019Zivanov_BayesianBIM]]
| Signal enhancement of SPA particles
| Bayesian correction of beam induced movement
|-  
|-  


| Paper
| Paper
| [[2023Strelak_MovieAlignment]]
| [[2020Bepler_TopazDenoise]]
| Comparison of movie alignment programs
| Preprocessing of micrographs for better picking
|-  
|-  


|}
| Paper
 
| [[2020Chung_2SDR]]
=== 2D Alignment ===
| PCA to denoise particles
 
|-
{|


| Paper
| Paper
| [[1981Frank_Averaging]]
| [[2020Chung_Prepro]]
| 2D averaging and phase residual
| Preprocessing of particles for better alignment
|-  
|-  


| Paper
| Conference
| [[1982Saxton_Averaging]]
| [[2020Huang_SuperResolution]]
| 2D averaging using correlation
| Deep learning superresolution combination of frames
|-  
|-  


| Paper
| Paper
| [[1998Sigworth_ML2D]]
| [[2020Palovcak_noise2noise]]
| Maximum likelihood alignment in 2D
| Noise2noise denoising of micrographs
|-  
|-  


| Paper
| Paper
| [[2003Cong_FRM2D]]
| [[2020Strelak_FlexAlign]]
| Fast Rotational Matching in 2D
| Continuous deformation model for aligning movie frames
|-  
|-  


| Paper
| Conference
| [[2005Cong_FRM2D]]
| [[2021Fan_Denoising]]
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
| Particle denoising using vector diffusion maps
|-  
|-  


| Paper
| Paper
| [[2005Scheres_ML2D]]
| [[2022Heymann_ProgressiveSSNR]]
| Multireference alignment and classification in 2D
| Progressive SSNR to assess quality and radiation damage
|-  
|-  


| Paper
| Paper
| [[2016Aguerrebere_Limits]]
| [[2022Shi_Denoising]]
| Fundamental limits of 2D translational alignment
| Contrast estimation and denoising in SPA
|-  
|-  


| Paper
| Paper
| [[2010Sorzano_CL2D]]
| [[2023Huang_ZSSR]]
| Multireference alignment and classification in 2D
| Multiple image super-resolution, upsampling with deep learning
|-  
 
| Conference
| [[2017Anoshina_Correlation]]
| New correlation measure for aligning images
|-  
|-  


| Paper
| Paper
| [[2019Radermacher_Correlation]]
| [[2023Marshall_PCA]]
| On the properties of cross correlation for the alignment of images
| Fast PCA on single particle images
|-  
|-  


| Paper
| Paper
| [[2020Lederman_representation]]
| [[2023Sharon_Enhancement]]
| A representation theory perspective of alignment and classification
| Signal enhancement of SPA particles
|-  
|-  


| Paper
| Paper
| [[2020Marshall_Invariants]]
| [[2023Strelak_MovieAlignment]]
| Recovery of an image from its invariants
| Comparison of movie alignment programs
|-  
|-  


| Paper
| Paper
| [[2021Chen_Fast]]
| [[2023Zhang_Denoising]]
| Fast alignment through Power Spectrum
| Single Particle denoising using Deep Convolutional autoencoder and K-means++
|-  
 
| Conference
| [[2021Chung_CryoRALIB]]
| Image alignment acceleration
|-  
|-  


| Paper
| Paper
| [[2021Heimowitz_Centering]]
| [[2024Li_Subtraction]]
| Centering noisy images
| Subtraction of membrane signal in SPA
|-  
|-  


|}
|}


=== 2D Classification and clustering ===
=== 2D Alignment ===


{|
{|


| Paper
| Paper
| [[1981VanHeel_MSA]]
| [[1981Frank_Averaging]]
| Multivariate Statistical Analysis
| 2D averaging and phase residual
|-  
|-  


| Paper
| Paper
| [[1984VanHeel_MSA]]
| [[1982Saxton_Averaging]]
| Multivariate Statistical Analysis
| 2D averaging using correlation
|-  
|-  


| Paper
| Paper
| [[2005Scheres_ML2D]]
| [[1998Sigworth_ML2D]]
| Multireference alignment and classification in 2D
| Maximum likelihood alignment in 2D
|-  
|-  


| Paper
| Paper
| [[2010Sorzano_CL2D]]
| [[2003Cong_FRM2D]]
| Multireference alignment and classification in 2D
| Fast Rotational Matching in 2D
|-  
|-  


| Paper
| Paper
| [[2011Singer_DiffusionMaps]]
| [[2005Cong_FRM2D]]
| Classification in 2D based on graph analysis of the projections
| Fast Rotational Matching in 2D introduced in a 3D Alignment algorithm
|-  
|-  


| Paper
| Paper
| [[2012Yang_ISAC]]
| [[2005Scheres_ML2D]]
| Iterative Stable Alignment and clustering
| Multireference alignment and classification in 2D
|-  
|-  


| Paper
| Paper
| [[2014Sorzano_Outlier]]
| [[2016Aguerrebere_Limits]]
| Outlier detection in 2D classifications.
| Fundamental limits of 2D translational alignment
|-  
|-  


| Paper
| Paper
| [[2014Zhao_Aspire]]
| [[2010Sorzano_CL2D]]
| Fast classification based on rotational invariants and vector diffusion maps
| Multireference alignment and classification in 2D
|-  
|-  


| Paper
| Conference
| [[2015Huang_Robust]]
| [[2017Anoshina_Correlation]]
| Robust w-estimators of 2D classes
| New correlation measure for aligning images
|-  
|-  


| Paper
| Paper
| [[2016Kimanius_Accelerated]]
| [[2019Radermacher_Correlation]]
| GPU Accelerated image classification and high-resolution refinement
| On the properties of cross correlation for the alignment of images
|-  
|-  


| Paper
| Paper
| [[2016Reboul_Stochastic]]
| [[2020Lederman_representation]]
| Stochastic Hill Climbing for calculating 2D classes
| A representation theory perspective of alignment and classification
|-  
|-  


| Conference
| Paper
| [[2017Bhamre_Mahalanobis]]
| [[2020Marshall_Invariants]]
| 2D classification using Mahalanobis distance
| Recovery of an image from its invariants
|-  
|-  


| Paper
| Paper
| [[2017Wu_GTM]]
| [[2021Chen_Fast]]
| 2D classification using Generative Topographic Mapping
| Fast alignment through Power Spectrum
|-  
|-  


| Conference
| Conference
| [[2018Boumal_SinglePass]]
| [[2021Chung_CryoRALIB]]
| Single pass classification
| Image alignment acceleration
|-  
|-  


| Conference
| Paper
| [[2018Shuo_Network]]
| [[2021Heimowitz_Centering]]
| 2D Clustering by network metrics
| Centering noisy images
|-  
|-  


| Conference
| Conference
| [[2020Miolane_VAEGAN]]
| [[2022Bendory_Complexity]]
| 2D Analysis by deep learning
| Computational complexity of multireference image alignment
|-
 
| Conference
| [[2021Rao_Wasserstein]]
| Wasserstein K-Means for Clustering Tomographic Projections
|-  
|-  


| Paper
| Paper
| [[2022Wang_Spectral]]
| [[2024Bendory_Complexity]]
| 2D classification with spectral clustering
| Computational complexity of multireference image alignment
|-  
|-  


| Paper
| Paper
| [[2022Zhang_DRVAE]]
| [[2024Bai_NUFT]]
| 2D classification with deep learning and K-means++
| 2D Image classification based on the Non-uniform Fourier Transform
|-  
|-  


| Paper
| Paper
| [[2023Chen_Joint]]
| [[2025Kapnulin_Outlier]]
| 2D classification with deep learning and joint unsupervised difference learning
| 2D Outlier rejection based on radial averages
|-  
|-  
|}
|}


=== 3D Alignment ===
=== 2D Classification and clustering ===


{|
{|


| Paper
| Paper
| [[1980Kam_AutoCorrelation]]
| [[1981VanHeel_MSA]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
| Multivariate Statistical Analysis
|-  
|-  


| Paper
| Paper
| [[1986Goncharov_CommonLines]]
| [[1984VanHeel_MSA]]
| Angular assignment using common lines (reference free)
| Multivariate Statistical Analysis
|-  
|-  


| Paper
| Paper
| [[1987VanHeel_CommonLines]]
| [[2005Scheres_ML2D]]
| Angular assignment using common lines (reference free)
| Multireference alignment and classification in 2D
|-  
|-  


| Paper
| Paper
| [[1988Provencher_Simultaneous]]
| [[2010Sorzano_CL2D]]
| Simultaneaous alignment and reconstruction
| Multireference alignment and classification in 2D
|-  
|-  


| Paper
| Paper
| [[1988Radermacher_RCT]]
| [[2011Singer_DiffusionMaps]]
| Random Conical Tilt and Single axis tilt
| Classification in 2D based on graph analysis of the projections
|-  
|-  


| Paper
| Paper
| [[1988Vogel_Simultaneous]]
| [[2012Yang_ISAC]]
| Simultaneaous alignment and reconstruction
| Iterative Stable Alignment and clustering
|-  
|-  


| Paper
| Paper
| [[1990Gelfand_Moments]]
| [[2014Sorzano_Outlier]]
| Angular assignment using moments (reference free)
| Outlier detection in 2D classifications.
|-  
|-  


| Paper
| Paper
| [[1990Goncharov_Moments]]
| [[2014Zhao_Aspire]]
| Angular assignment using moments (reference free)
| Fast classification based on rotational invariants and vector diffusion maps
|-  
|-  


| Paper
| Paper
| [[1990Harauz_Quaternions]]
| [[2015Huang_Robust]]
| Use of quaternions to represent rotations
| Robust w-estimators of 2D classes
|-  
|-  


| Paper
| Paper
| [[1994Penczek_Real]]
| [[2016Kimanius_Accelerated]]
| Angular assignment using projection matching in real space
| GPU Accelerated image classification and high-resolution refinement
|-  
|-  


| Paper
| Paper
| [[1994Radermacher_Radon]]
| [[2016Reboul_Stochastic]]
| Angular assignment in Radon space
| Stochastic Hill Climbing for calculating 2D classes
|-  
|-  


| Paper
| Conference
| [[1996Penczek_CommonLines]]
| [[2017Bhamre_Mahalanobis]]
| Angular assignment using common lines (reference free)
| 2D classification using Mahalanobis distance
|-  
|-  


| Paper
| Paper
| [[2003Rosenthal_DPR]]
| [[2017Wu_GTM]]
| Angular assignment using DPR
| 2D classification using Generative Topographic Mapping
|-  
|-  


| Paper
| Conference
| [[2004Sorzano_Wavelet]]
| [[2018Boumal_SinglePass]]
| Angular assignment in the wavelet space.
| Single pass classification
|-  
|-  


| Paper
| Conference
| [[2005Jonic_Splines]]
| [[2018Shuo_Network]]
| Angular assignment in Fourier space using spline interpolation.
| 2D Clustering by network metrics
|-  
|-  


| Paper
| Paper
| [[2005Yang_Simultaneous]]
| [[2020Ma_RotationInvariant]]
| Simultaneaous alignment and reconstruction
| 2D heterogeneity determination by rotation invariant features
|-  
|-  


| Paper
| Conference
| [[2006Ogura_SimulatedAnnealing]]
| [[2020Miolane_VAEGAN]]
| Angular asignment by simulated annealing
| 2D Analysis by deep learning
|-  
|-  


| Paper
| Conference
| [[2007Grigorieff_Continuous]]
| [[2021Rao_Wasserstein]]
| Continuous angular assignment in Fourier space
| Wasserstein K-Means for Clustering Tomographic Projections
|-  
|-  


| Paper
| Paper
| [[2010Jaitly_Bayesian]]
| [[2022Vilela_Feret]]
| Angular assignment by a Bayesian method and annealing
| 2D heterogeneity detection through Feret signatures
|-
|-  


| Paper
| Paper
| [[2010Sanz_Random]]
| [[2022Wang_Spectral]]
| Random model method
| 2D classification with spectral clustering
|-
|-  


| Paper
| Paper
| [[2010Singer_Voting]]
| [[2022Zhang_DRVAE]]
| Detecting consistent common lines by voting (reference free)
| 2D classification with deep learning and K-means++
|-
|-  


| Paper
| Paper
| [[2011Singer_SDP]]
| [[2023Chen_Joint]]
| Angular assignment by semidefinite programming and eigenvectors (reference free)
| 2D classification with deep learning and joint unsupervised difference learning
|-
|-  


| Paper
| Conference
| [[2012Giannakis_Scattering]]
| [[2023Weiss_Noise]]
| Construction of an initial volume, reference free, by graph analysis of the projections
| Identifying non-particles with probabilistic PCA
|-
|-  


| Paper
| Paper
| [[2012Shkolnisky_Sync]]
| [[2024Tang_SimCryoCluster]]
| Angular assignment by synchronization of rotations (reference free)
| SimCryoCluster: 2D classification in SPA using a deep clustering method
|-
|-  


| Paper
| Paper
| [[2013Elmlund H_PRIME]]
| [[2025Bai_NUDFT]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
| 2D Classification in SPA using the Non-uniform DFT
|-  
|-  


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


| Paper
=== 3D Alignment ===
| [[2014Vargas_RANSAC]]
|  Initial model using RANSAC (reference free)
|-


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


| Paper
| Paper
| [[2015Singer_Kam]]
| [[1980Kam_AutoCorrelation]]
| Reconstruction without angular assignment from autocorrelation function (reference free)
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-  
|-  


| Paper
| Paper
| [[2015Sorzano_Significant]]
| [[1986Goncharov_CommonLines]]
| Statistical approach to the initial volume estimation (reconstruct significant)
| Angular assignment using common lines (reference free)
|-  
|-  


| Paper
| Paper
| [[2016Cossio_BayesianGPU]]
| [[1987VanHeel_CommonLines]]
| GPU implementation of the Bayesian 3D reconstruction approach
| Angular assignment using common lines (reference free)
|-  
|-  


| Conference
| Paper
| [[2016Michels_Heterogeneous]]
| [[1988Provencher_Simultaneous]]
| Initial volume in the presence of heterogeneity
| Simultaneaous alignment and reconstruction
|-  
|-  


| Paper
| Paper
| [[2016Pragier_Graph]]
| [[1988Radermacher_RCT]]
| Graph partitioning approach to angular reconstitution
| Random Conical Tilt and Single axis tilt
|-  
|-  


| Paper
| Paper
| [[2017Greenberg_CommonLines]]
| [[1988Vogel_Simultaneous]]
| Common lines for reference free ab-initio reconstruction
| Simultaneaous alignment and reconstruction
|-  
|-  


| Paper
| Paper
| [[2018Sorzano_Highres]]
| [[1990Gelfand_Moments]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
| Angular assignment using moments (reference free)
|-  
|-  


| Paper
| Paper
| [[2018Sorzano_Swarm]]
| [[1990Goncharov_Moments]]
| Consensus of several initial volumes by swarm optimization
| Angular assignment using moments (reference free)
|-  
|-  


| Paper
| Paper
| [[2019Zehni_Joint]]
| [[1990Harauz_Quaternions]]
| Continuous angular refinement and reconstruction
| Use of quaternions to represent rotations
|-  
|-  


| Paper
| Paper
| [[2019Zehni_Joint]]
| [[1994Penczek_Real]]
| Continuous angular refinement and reconstruction
| Angular assignment using projection matching in real space
|-  
|-  


| Paper
| Paper
| [[2020Sharon_NonUniformKam]]
| [[1994Radermacher_Radon]]
| Reconstruction and angular distribution estimation without angular assignment (reference free)
| Angular assignment in Radon space
|-  
|-  


| Paper
| Paper
| [[2020Xie_Network]]
| [[1996Penczek_CommonLines]]
| Angular assignment considering a network of assignments
| Angular assignment using common lines (reference free)
|-  
|-  


| Paper
| Paper
| [[2021Jimenez_DeepAlign]]
| [[2003Rosenthal_DPR]]
| Angular alignment using deep learning
| Angular assignment using DPR
|-  
|-  


| Paper
| Paper
| [[2021Kojima_Preferred]]
| [[2004Sorzano_Wavelet]]
| Identification of preferred orientations
| Angular assignment in the wavelet space.
|-  
|-  


| Conference
| Paper
| [[2021Nashed_CryoPoseNet]]
| [[2005Jonic_Splines]]
| CryoPoseNet: Angular alignment with deep learning
| Angular assignment in Fourier space using spline interpolation.
|-  
|-  


| Conference
| Paper
| [[2021Zhong_CryoDRGN2]]
| [[2005Yang_Simultaneous]]
| CryoDRGN2: Angular alignment with deep learning
| Simultaneaous alignment and reconstruction
|-  
|-  


| Paper
| Paper
| [[2022Lu_SphericalEmbeddings]]
| [[2006Ogura_SimulatedAnnealing]]
| Angular assignment through common lines and spherical embeddings
| Angular asignment by simulated annealing
|-  
|-  


| Paper
| Paper
| [[2022Wang_Thunder]]
| [[2007Grigorieff_Continuous]]
| Angular assignment implementation in GPU
| Continuous angular assignment in Fourier space
|-  
|-  


| Paper
| Paper
| [[2024Chung_CryoForum]]
| [[2010Jaitly_Bayesian]]
| CryoForum: Angular assignment with uncertainty estimation using neural networks
| Angular assignment by a Bayesian method and annealing
|-  
|-


|}
| Paper
 
| [[2010Sanz_Random]]
=== 3D Reconstruction ===
| Random model method
{|
|-


| Paper
| Paper
| [[1972Gilbert_SIRT]]
| [[2010Singer_Voting]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
| Detecting consistent common lines by voting (reference free)
|-  
|-


| Paper
| Paper
| [[1973Herman_ART]]
| [[2011Singer_SDP]]
| Algebraic Reconstruction Technique (ART)
| Angular assignment by semidefinite programming and eigenvectors (reference free)
|-  
|-


| Paper
| Paper
| [[1980Kam_SphericalHarmonics]]
| [[2012Giannakis_Scattering]]
| 3D Reconstruction using spherical harmonics
| Construction of an initial volume, reference free, by graph analysis of the projections
|-  
|-


| Paper
| Paper
| [[1984Andersen_SART]]
| [[2012Shkolnisky_Sync]]
| Simultaneous Algebraic Reconstruction Technique (SART)
| Angular assignment by synchronization of rotations (reference free)
|-  
|-


| Paper
| Paper
| [[1986Harauz_FBP]]
| [[2013Elmlund H_PRIME]]
| Exact filters for Filtered Back Projection
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-  
|-  


| Chapter
| Paper
| [[1992Radermacher_WBP]]
| [[2013Wang_LUD]]
| Exact filters for Weighted Back Projection
| Angular assignment by least unsquared deviations (reference free)
|-  
|-


| Paper
| Paper
| [[1997Zhu_RecCTF]]
| [[2014Vargas_RANSAC]]
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
| Initial model using RANSAC (reference free)
|-  
|-


| Paper
| Paper
| [[1998Boisset_Uneven]]
| [[2015Joubert_Pseudoatoms]]
| Artifacts in SIRT and WBP under uneven angular distributions
| Initial model based on pseudo-atoms
|-  
|-  


| Paper
| Paper
| [[1998Marabini_ART]]
| [[2015Singer_Kam]]
| Algebraic Reconstruction Technique with blobs (Xmipp)
| Reconstruction without angular assignment from autocorrelation function (reference free)
|-  
|-  


| Paper
| Paper
| [[2001Sorzano_Uneven]]
| [[2015Sorzano_Significant]]
| Free parameter selection under uneven angular distributions
| Statistical approach to the initial volume estimation (reconstruct significant)
|-  
|-  


| Paper
| Paper
| [[2005Sorzano_Parameters]]
| [[2016Cossio_BayesianGPU]]
| Free parameter selection for optimizing multiple tasks
| GPU implementation of the Bayesian 3D reconstruction approach
|-  
|-  


| Paper
| Conference
| [[2008Sorzano_Constraints]]
| [[2016Michels_Heterogeneous]]
| Mass, surface, positivity and symmetry constraints for real-space algorithms
| Initial volume in the presence of heterogeneity
|-  
|-  


| Paper
| Paper
| [[2009Bilbao_ParallelART]]
| [[2016Pragier_Graph]]
| Efficient parallelization of ART
| Graph partitioning approach to angular reconstitution
|-  
|-  


| Paper
| Paper
| [[2011Li_GradientFlow]]
| [[2017Greenberg_CommonLines]]
| Regularized 3D Reconstruction by Gradient Flow
| Common lines for reference free ab-initio reconstruction
|-  
|-  


| Paper
| Paper
| [[2011Vonesch_Wavelets]]
| [[2018Sorzano_Highres]]
| Fast wavelet-based 3D reconstruction
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-  
|-  


| Paper
| Paper
| [[2012Gopinath_ShapeRegularization]]
| [[2018Sorzano_Swarm]]
| Regularized 3D Reconstruction by Shape information
| Consensus of several initial volumes by swarm optimization
|-  
|-  


| Paper
| Paper
| [[2012Kucukelbir_adaptiveBasis]]
| [[2019Zehni_Joint]]
| 3D reconstruction in an adaptive basis promoting sparsity
| Continuous angular refinement and reconstruction
|-  
|-  


| Paper
| Paper
| [[2012Sindelar_NoiseReduction]]
| [[2019Zehni_Joint]]
| Optimal noise reduction in 3D reconstructions
| Continuous angular refinement and reconstruction
|-  
|-  


| Paper
| Paper
| [[2013Elmlund H_PRIME]]
| [[2020Sharon_NonUniformKam]]
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
| Reconstruction and angular distribution estimation without angular assignment (reference free)
|-  
|-  


| Paper
| Paper
| [[2013Lyumkis_Optimod]]
| [[2020Xie_Network]]
| Construction of initial volumes with Optimod
| Angular assignment considering a network of assignments
|-  
|-  


| Paper
| Paper
| [[2013Wang FIRM]]
| [[2021Jimenez_DeepAlign]]
| Fast 3D reconstruction in Fourier domain
| Angular alignment using deep learning
|-  
|-  


| Paper
| Paper
| [[2014Kunz_SART_OS]]
| [[2021Kojima_Preferred]]
| Simultaneous ART with OS
| Identification of preferred orientations
|-  
|-  


| Paper
| Conference
| [[2015Abrishami_Fourier]]
| [[2021Nashed_CryoPoseNet]]
| 3D Reconstruction in Fourier space
| CryoPoseNet: Angular alignment with deep learning
|-  
|-  


| Paper
| Conference
| [[2015Dvornek_SubspaceEM]]
| [[2021Zhong_CryoDRGN2]]
| Fast Maximum a posteriori
| CryoDRGN2: Angular alignment with deep learning
|-  
|-  


| Paper
| Conference
| [[2015Moriya_Bayesian]]
| [[2022Levy_CryoAI]]
| Bayesian approach to suppress limited angular artifacts
| CryoAI: Angular assignment through neural network
|-  
|-  


| Paper
| Paper
| [[2015Xu_GeometricFlow]]
| [[2022Lian_Neural]]
| Multi-scale geometric flow
| Angular assignment through neural network
|-  
|-  


| Arxiv
| Paper
| [[2016Ye_Cohomology]]
| [[2022Lu_SphericalEmbeddings]]
| Cohomology properties of 3D reconstruction
| Angular assignment through common lines and spherical embeddings
|-  
|-  


| Paper
| Paper
| [[2017Barnett_Marching]]
| [[2022Wang_Thunder]]
| Initial volume through frequency marching
| Angular assignment implementation in GPU
|-  
|-  


| Paper
| Conference
| [[2017Punjani_CryoSPARC]]
| [[2023Cesa_Alignment]]
| CryoSPARC
| 3D alignment based on deep learning and equivariant representations
|-  
|-  


| Paper
| Paper
| [[2017Punjani_CryoSPARCTheory]]
| [[2023Harpaz_Alignment]]
| Theory related to CryoSPARC
| Fast alignment of two maps using common lines
|-  
|-  


| Paper
| Paper
| [[2017Sorzano_SurveyIterative]]
| [[2023Ling_Synch]]
| Survey of iterative reconstruction methods for EM
| Synchronization of projection directions
|-  
|-  


| Paper
| Paper
| [[2018Bartesaghi_Refinement]]
| [[2023Rangan_Fast]]
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
| Fast angular assignment using Fourier-Bessel
|-  
|-  


| Paper
| Paper
| [[2018Hu_ParticleFilter]]
| [[2023Riahi_Transport]]
| A particle filter framework for 3D reconstruction
| Alignment of two 3D maps using Wasserstein's distance
|-  
|-  


| Conference
| Paper
| [[2018Levin_Kam]]
| [[2024Chung_CryoForum]]
| Ab initio reconstruction by autocorrelation analysis
| CryoForum: Angular assignment with uncertainty estimation using neural networks
|-  
|-  


| Conference
| Paper
| [[2018Michels_RBF]]
| [[2024Muller_Common]]
| Ab-initio reconstruction with radial basis functions
| Initial volume in the presence of heterogeneity using common lines
|-  
|-  


| Paper
| Paper
| [[2018Reboul_Simple]]
| [[2024Nottelet_Feret]]
| Ab initio reconstruction with Simple
| Feret signature to detect preferred orientations and misclassified images
|-  
|-  


| Paper
| Paper
| [[2018Sorzano_Highres]]
| [[2024Sanchez_Cesped]]
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
| CESPED: A benchmark for supervised particle pose estimation
|-  
|-  


| Paper
| Conference
| [[2018Sorzano_Swarm]]
| [[2024Shekarforoush_CryoSPIN]]
| Consensus of several initial volumes by swarm optimization
| CryoSpin: Semi-amortized image alignment using deep learning
|-  
|-  


| Paper
| Paper
| [[2018Zhu_Ewald]]
| [[2024Singer_Wasserstein]]
| 3D Reconstruction with Ewald sphere correction
| Alignment of two 3D maps using Wasserstein's distance
|-  
|-  


| Paper
| Paper
| [[2019Gomez_Initial]]
| [[2024Titarenko_optimal]]
| Construction of initial models
| Optimal 3D angular sampling
|-
 
| Master
| [[2019Havelkova_Regularization]]
| Regularization methods in 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2019Wilkinson_Scales]]
| [[2024Wang_CommonLines]]
| Combining data acquired at different scales
| 3D Alignment by common lines
|-  
|-  


| Paper
| Paper
| [[2020Alazzawi_Auto]]
| [[2024Zhang_Kam]]
| Automatic full processing of micrographs to yield a 3D reconstruction
| Distance between maps without aligning them
|-  
|-  
|}
=== 3D Reconstruction ===
{|


| Paper
| Paper
| [[2020Pan_TV]]
| [[1972Gilbert_SIRT]]
| 3D Reconstruction with total variation regularization
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-  
|-  


| Paper
| Paper
| [[2020Punjani_NonUniform]]
| [[1973Herman_ART]]
| Non-uniform refinement
| Algebraic Reconstruction Technique (ART)
|-  
|-  


| Paper
| Paper
| [[2020Ramlaul_Sidesplitter]]
| [[1980Kam_SphericalHarmonics]]
| Local filtering along the reconstruction iterations
| 3D Reconstruction using spherical harmonics
|-  
|-  


| Paper
| Paper
| [[2020Xie_Automatic]]
| [[1984Andersen_SART]]
| Automatic 3D reconstruction from projections
| Simultaneous Algebraic Reconstruction Technique (SART)
|-  
|-  


| Conference
| Paper
| [[2020Venkatakrishnan_MBIR]]
| [[1986Harauz_FBP]]
| Model based image reconstruction
| Exact filters for Filtered Back Projection
|-  
|-  


| Paper
| Chapter
| [[2020Zhou_AutomaticSelection]]
| [[1992Radermacher_WBP]]
| Automatic selection of particles for 3D reconstruction
| Exact filters for Weighted Back Projection
|-  
|-  


| Paper
| Paper
| [[2021Abrishami_Localized]]
| [[1997Zhu_RecCTF]]
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
| 3D Reconstruction (SIRT like) and simultaneous CTF correction
|-  
|-  


| Paper
| Paper
| [[2021Gupta_CryoGAN]]
| [[1998Boisset_Uneven]]
| 3D Reconstruction via Generative Adversarial Learning
| Artifacts in SIRT and WBP under uneven angular distributions
|-  
|-  


| Paper
| Paper
| [[2021Luo_Opus]]
| [[1998Marabini_ART]]
| 3D Reconstruction with a sparse and smoothness constraint
| Algebraic Reconstruction Technique with blobs (Xmipp)
|-  
|-  


| Paper
| Paper
| [[2021Kimanius_PriorKnowledge]]
| [[2001Sorzano_Uneven]]
| Incorporation of prior knowledge during 3D reconstruction
| Free parameter selection under uneven angular distributions
|-  
|-  


| Paper
| Paper
| [[2021Sorzano_Uneven]]
| [[2005Sorzano_Parameters]]
| Algorithmic robustness to uneven angular distributions
| Free parameter selection for optimizing multiple tasks
|-  
|-  


| Paper
| Paper
| [[2022Havelkova_regularization]]
| [[2008Sorzano_Constraints]]
| Regularization of iterative reconstruction algorithms
| Mass, surface, positivity and symmetry constraints for real-space algorithms
|-  
|-  


| Conference
| Paper
| [[2022Kimanius_Sparse]]
| [[2009Bilbao_ParallelART]]
| Sparse Fourier backpropagation
| Efficient parallelization of ART
|-  
|-  


| Paper
| Paper
| [[2023Bendory_Autocorrelation]]
| [[2011Li_GradientFlow]]
| Initial volume through autocorrelation analysis with sparsity constraints
| Regularized 3D Reconstruction by Gradient Flow
|-  
|-  


| Paper
| Paper
| [[2023Herreros_ZART]]
| [[2011Vonesch_Wavelets]]
| Correction of continuous heterogeneity during the 3D reconstruction
| Fast wavelet-based 3D reconstruction  
|-  
|-  


| Paper
| Paper
| [[2023Rangan_AbInitio]]
| [[2012Gopinath_ShapeRegularization]]
| Robust ab initio reconstruction
| Regularized 3D Reconstruction by Shape information
|-  
|-  


| Paper
| Paper
| [[2023Zhu_CryoSieve]]
| [[2012Kucukelbir_adaptiveBasis]]
| CryoSieve: Selection of the best particles to reconstruct
| 3D reconstruction in an adaptive basis promoting sparsity
|-  
|-  


| Paper
| Paper
| [[2024Huang_CryoNefen]]
| [[2012Sindelar_NoiseReduction]]
| 3D reconstruction in real space with neural networks
| Optimal noise reduction in 3D reconstructions
|-  
|-  


| Paper
| Paper
| [[2024Liu_kinetic]]
| [[2013Elmlund H_PRIME]]
| A kinetic model for the resolution of the initial model using common lines
| PRIME: Probabilistic Initial 3D Model Generation for Single-Particle Cryo-Electron Microscopy
|-  
|-  
|}
=== 3D Heterogeneity ===
{|


| Paper
| Paper
| [[2004White_Size]]
| [[2013Lyumkis_Optimod]]
| Heterogeneity classification of differently sized images
| Construction of initial volumes with Optimod
|-  
|-  


| Paper
| Paper
| [[2006Penczek_Bootstrap]]
| [[2013Wang FIRM]]
| 3D heterogeneity through bootstrap
| Fast 3D reconstruction in Fourier domain
|-  
|-  


| Paper
| Paper
| [[2007Leschziner_Review]]
| [[2014Kunz_SART_OS]]
| Review of 3D heterogeneity handling algorithms
| Simultaneous ART with OS
|-  
|-  


| Paper
| Paper
| [[2007Scheres_ML3D]]
| [[2015Abrishami_Fourier]]
| Maximum Likelihood alignment and classification in 3D
| 3D Reconstruction in Fourier space
|-  
|-  


| Paper
| Paper
| [[2008Herman_Graph]]
| [[2015Dvornek_SubspaceEM]]
| Classification by graph partitioning
| Fast Maximum a posteriori
|-  
|-  


| Paper
| Paper
| [[2009Spahn_Bootstrap]]
| [[2015Moriya_Bayesian]]
| 3D heterogeneity through bootstrap
| Bayesian approach to suppress limited angular artifacts
|-  
|-  


| Paper
| Paper
| [[2010Elmlund_AbInitio]]
| [[2015Xu_GeometricFlow]]
| Solving the initial volume problem with multiple conformations
| Multi-scale geometric flow
|-  
|-  


| Paper
| Arxiv
| [[2010Shatsky_MultiVariate]]
| [[2016Ye_Cohomology]]
| Multivariate Statistical Analysis
| Cohomology properties of 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2012Scheres_Bayesian]]
| [[2017Barnett_Marching]]
| A Bayesian view on cryo-EM structure determination
| Initial volume through frequency marching
|-  
|-  


| Paper
| Paper
| [[2012Zheng_Covariance]]
| [[2017Punjani_CryoSPARC]]
| Estimation of the volume covariance
| CryoSPARC
|-  
|-  


| Paper
| Paper
| [[2013Wang_MLVariance]]
| [[2017Punjani_CryoSPARCTheory]]
| Maximum Likelihood estimate of the map variance
| Theory related to CryoSPARC
|-  
|-  


| Paper
| Paper
| [[2013Lyumkis D_FREALIGN]]
| [[2017Sorzano_SurveyIterative]]
| Likelihood-based classification of cryo-EM images using FREALIGN.
| Survey of iterative reconstruction methods for EM
|-
|-  


| Paper
| Paper
| [[2014Chen_Migration]]
| [[2018Bartesaghi_Refinement]]
| Particle migration analysis in 3D classification
| Refinement of CTF, frame weight and alignment for high resolution reconstruction
|-  
|-  


| Paper
| Paper
| [[2014Dashti_Brownian]]
| [[2018Hu_ParticleFilter]]
| Continuous heterogeneity through Brownian trajectories
| A particle filter framework for 3D reconstruction
|-  
|-  


| Paper
| Conference
| [[2014Schwander_manifold]]
| [[2018Levin_Kam]]
| Continuous heterogeneity through Manifold Analysis
| Ab initio reconstruction by autocorrelation analysis
|-  
|-  


| Paper
| Conference
| [[2014Jin_NMA]]
| [[2018Michels_RBF]]
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
| Ab-initio reconstruction with radial basis functions
|-  
|-  


| Paper
| Paper
| [[2015Anden_Covariance]]
| [[2018Reboul_Simple]]
| 3D Covariance matrix estimation for heterogeneity
| Ab initio reconstruction with Simple
|-  
|-  


| Paper
| Paper
| [[2015Bai_Focused]]
| [[2018Sorzano_Highres]]
| Focused classification
| New algorithm for 3D Reconstruction and alignment with emphasis on significance
|-  
|-  


| Paper
| Paper
| [[2015Katsevich_Covariance]]
| [[2018Sorzano_Swarm]]
| 3D Covariance matrix estimation for heterogeneity
| Consensus of several initial volumes by swarm optimization
|-  
|-  


| Paper
| Paper
| [[2015Klaholz_MRA]]
| [[2018Zhu_Ewald]]
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
| 3D Reconstruction with Ewald sphere correction
|-  
|-  


| Paper
| Paper
| [[2015Liao_Covariance]]
| [[2019Gomez_Initial]]
| Estimation of the 3D covariance from 2D projections
| Construction of initial models
|-  
|-  


| Paper
| Master
| [[2015Tagare_Direct]]
| [[2019Havelkova_Regularization]]
| Direct reconstruction of PCA components
| Regularization methods in 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2016Gong_Mechanical]]
| [[2019Wilkinson_Scales]]
| Mechanical model for macromolecules
| Combining data acquired at different scales
|-  
|-  


| Paper
| Paper
| [[2016Rawson_Movement]]
| [[2020Alazzawi_Auto]]
| Movement and flexibility
| Automatic full processing of micrographs to yield a 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2016Shan_Multibody]]
| [[2020Pan_TV]]
| Multibody refinement
| 3D Reconstruction with total variation regularization
|-  
|-  


| Paper
| Paper
| [[2016Sorzano_StructMap]]
| [[2020Punjani_NonUniform]]
| Sorting a discrete set of conformational states
| Non-uniform refinement
|-  
|-  


| Paper
| Paper
| [[2016Sorzano_Strain]]
| [[2020Ramlaul_Sidesplitter]]
| Calculate local stretches, strains and rotations from two conformational states
| Local filtering along the reconstruction iterations
|-  
|-  


| Paper
| Paper
| [[2017Punjani_CryoSPARC]]
| [[2020Xie_Automatic]]
| CryoSPARC
| Automatic 3D reconstruction from projections
|-  
|-  


| Paper
| Conference
| [[2017Schillbach_Warpcraft]]
| [[2020Venkatakrishnan_MBIR]]
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
| Model based image reconstruction
|-  
|-  


| Paper
| Paper
| [[2018Anden_Covariance]]
| [[2020Zhou_AutomaticSelection]]
| Structural Variability from Noisy Tomographic Projections
| Automatic selection of particles for 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2018Haselbach_FreeEnergy]]
| [[2021Abrishami_Localized]]
| Analysis of the free energy landscape through PCA
| Localized reconstruction in scipion expedites the analysis of symmetry mismatches in Cryo-EM data
|-  
|-  


| Paper
| Paper
| [[2018Nakane_MultiBody]]
| [[2021Gupta_CryoGAN]]
| Structural Variability through multi-body refinement
| 3D Reconstruction via Generative Adversarial Learning
|-  
|-  


| Paper
| Paper
| [[2019Serna_Review]]
| [[2021Luo_Opus]]
| Review of classification tools
| 3D Reconstruction with a sparse and smoothness constraint
|-  
|-  


| Paper
| Paper
| [[2018Solernou_FFEA]]
| [[2021Kimanius_PriorKnowledge]]
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
| Incorporation of prior knowledge during 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[2019Sorzano_Review]]
| [[2021Sorzano_Uneven]]
| Review of continuous heterogeneity biophysics
| Algorithmic robustness to uneven angular distributions
|-  
|-  


| Paper
| Paper
| [[2019Zhang_Local]]
| [[2022Havelkova_regularization]]
| Local variability and covariance
| Regularization of iterative reconstruction algorithms
|-
 
| Paper
| [[2020Dashti_Landscape]]
| Retrieving functional pathways from single particle snapshots
|-  
|-  


| Conference
| Conference
| [[2020Gupta_MultiCryoGAN]]
| [[2022Kimanius_Sparse]]
| Reconstruction of continuously heterogeneous structures with adversarial networks
| Sparse Fourier backpropagation
|-  
|-  


| Paper
| Paper
| [[2020Harastani_NMA]]
| [[2022Lan_RCT]]
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
| Random Conical Tilt without picking
|-  
|-  


| Paper
| Paper
| [[2020Maji_Propagation]]
| [[2023Bendory_Autocorrelation]]
| Propagation of conformational coordinates across angular space
| Initial volume through autocorrelation analysis with sparsity constraints
|-  
|-  


| Paper
| Paper
| [[2020Moscovich_DiffusionMaps]]
| [[2023Geva_AbInitio]]
| Heterogeneity analysis by diffusion maps and spectral volumes
| Initial volume through common lines for tetahedral and octahedral symmetry
|-  
|-  


| Paper
| Paper
| [[2020Seitz_Polaris]]
| [[2023Herreros_ZART]]
| Analysis of energy landscapes to find minimal action paths
| Correction of continuous heterogeneity during the 3D reconstruction
|-  
|-  


| Conference
| Paper
| [[2020Zhong_CryoDRGN]]
| [[2023Rangan_AbInitio]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
| Robust ab initio reconstruction
|-  
|-  


| Paper
| Paper
| [[2020Verbeke_Separation]]
| [[2023Zhu_CryoSieve]]
| Heterogeneity analysis by comparing common lines
| CryoSieve: Selection of the best particles to reconstruct
|-  
|-  


| Paper
| Paper
| [[2021Chen_GM]]
| [[2024Aiyer_Workflow]]
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
| Workflow for the reconstruction of tilted samples
|-  
|-  


| Paper
| Paper
| [[2021Giraldo_cryoBIFE]]
| [[2024Huang_CryoNefen]]
| A Bayesian approach to extracting free‑energy profiles
| 3D reconstruction in real space with neural networks
|-  
|-  


| Conference
| Paper
| [[2021Hamitouche_NMADL]]
| [[2024Liu_kinetic]]
| Continuous heterogeneity analysis through normal modes and deep learning
| A kinetic model for the resolution of the initial model using common lines
|-  
|-  


| Paper
| Paper
| [[2021Herreros_Zernikes3D]]
| [[2024Suder_Workflow]]
| Continuous heterogeneity analysis through Zernikes 3D
| Workflow for the reconstruction of subparticles in highly symmetrical objects
|-  
|-  


| Paper
| Paper
| [[2021Kazemi_Enrich]]
| [[2024Zhu_SIRM]]
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
| Reconstruction strategy and weights to fight preferred orientations
|-  
|-  


| Paper
| Paper
| [[2021Matsumoto_DEFmap]]
| [[2025Liu_SpIsonet]]
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
| Deep learning approach to fighting preferential orientations during 3D reconstruction
|-  
|-  


| Chapter
| Paper
| [[2021Nakasako_Landscape]]
| [[2025Singh_Mismatch]]
| Estimation of free-energy landscape from images
| Image processing workflow to address particles with symmetry mismatches
|-  
|-  


| Paper
| Paper
| [[2021Punjani_3DVA]]
| [[2025Van_Probabilistic]]
| 3D Variability analysis from images
| Multireference initial volume reconstruction in SPA
|-  
|-  


| Paper
| Paper
| [[2021Sorzano_PCA]]
| [[2025Woollard_InstaMap]]
| PCA is limited to low-resolution
| InstaMap: 3D reconstruction using neural networks
|-  
|-  
|}
=== 3D Heterogeneity ===
{|


| Paper
| Paper
| [[2021Zhong_CryoDRGN]]
| [[2004White_Size]]
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
| Heterogeneity classification of differently sized images
|-  
|-  


| Paper
| Paper
| [[2022Ecoffet_MorphOT]]
| [[2006Penczek_Bootstrap]]
| More physically plausible morphing between two states
| 3D heterogeneity through bootstrap
|-  
|-  


| Paper
| Paper
| [[2022Gomez_Hierarchical]]
| [[2007Leschziner_Review]]
| Hierarchical classification of particles
| Review of 3D heterogeneity handling algorithms
|-  
|-  


| Paper
| Paper
| [[2022Hamitouche_DeepHEMNMA]]
| [[2007Scheres_ML3D]]
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
| Maximum Likelihood alignment and classification in 3D
|-  
|-  


| Conference
| Paper
| [[2022Levy_CryoFire]]
| [[2008Herman_Graph]]
| CryoFire: heterogeneity and alignment through amortized inference
| Classification by graph partitioning
|-  
|-  


| Paper
| Paper
| [[2022Rabuck_Quant]]
| [[2009Spahn_Bootstrap]]
| Workflow for discrete heterogeneity analysis
| 3D heterogeneity through bootstrap
|-  
|-  


| Paper
| Paper
| [[2022Skalidis_Endogenous]]
| [[2010Elmlund_AbInitio]]
| AI tools to recognize proteins in cellular fractions
| Solving the initial volume problem with multiple conformations
|-  
|-  


| Paper
| Paper
| [[2022Wu_Manifold]]
| [[2010Shatsky_MultiVariate]]
| Continuous heterogeneity through manifold learning
| Multivariate Statistical Analysis
|-  
|-  


| Paper
| Paper
| [[2022Zhou_Data]]
| [[2012Scheres_Bayesian]]
| Determination of the number of discrete 3D classes
| A Bayesian view on cryo-EM structure determination
|-  
|-  


| Paper
| Paper
| [[2023Barchet_Focused]]
| [[2012Zheng_Covariance]]
| Applications and strategies in focused classification and refinement
| Estimation of the volume covariance
|-  
|-  


| Paper
| Paper
| [[2023Chen_GMM]]
| [[2013Wang_MLVariance]]
| Continuous heterogeneity analysis with GMMs and neural networks
| Maximum Likelihood estimate of the map variance
|-  
|-  


| Paper
| Paper
| [[2023Dsouza_benchmark]]
| [[2013Lyumkis D_FREALIGN]]
| Benchmark analysis of various continuous heterogeneity algorithms
| Likelihood-based classification of cryo-EM images using FREALIGN.
|-  
|-


| Paper
| Paper
| [[2023Esteve_Spectral]]
| [[2014Chen_Migration]]
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
| Particle migration analysis in 3D classification
|-  
|-  


| Paper
| Paper
| [[2023Fernandez_Subtraction]]
| [[2014Dashti_Brownian]]
| Subtraction of unwanted signals to improve classification and alignment
| Continuous heterogeneity through Brownian trajectories
|-  
|-  


| Paper
| Paper
| [[2023Herreros_Hub]]
| [[2014Schwander_manifold]]
| Flexibility hub: an integrative platform for continuous heterogeneity
| Continuous heterogeneity through Manifold Analysis
|-  
|-  


| Paper
| Paper
| [[2023Luo_OpusDSD]]
| [[2014Jin_NMA]]
| OPUS DSD: a neural network approach to continuous heterogeneity
| HEMNMA: Continuous heterogeneity through Normal Mode Analysis
|-  
|-  


| Paper
| Paper
| [[2023Kinman_Analysis]]
| [[2015Anden_Covariance]]
| Analysis of the continuous heterogeneity results of CryoDrgn
| 3D Covariance matrix estimation for heterogeneity  
|-  
|-  


| Paper
| Paper
| [[2023Matsumoto_DEFmap]]
| [[2015Bai_Focused]]
| Quantitative analysis of the prediction of RMSF from a map using DefMap
| Focused classification
|-  
|-  


| Paper
| Paper
| [[2023Punjani_3DFlex]]
| [[2015Katsevich_Covariance]]
| Continuous heterogeneity through 3DFlex
| 3D Covariance matrix estimation for heterogeneity  
|-  
|-  


| Paper
| Paper
| [[2023Seitz_Geometric]]
| [[2015Klaholz_MRA]]
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
| Multivariate Statistical Analysis of Jackknife and Bootstrapping on random subsets
|-  
|-  


| Paper
| Paper
| [[2023Seitz_ESPER]]
| [[2015Liao_Covariance]]
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
| Estimation of the 3D covariance from 2D projections
|-  
|-  


| Paper
| Paper
| [[2023Tang_Reweighting]]
| [[2015Tagare_Direct]]
| Ensemble reweighting using Cryo-EM particles
| Direct reconstruction of PCA components
|-  
|-  


| Paper
| Paper
| [[2023Vuillemot_MDSPACE]]
| [[2016Gong_Mechanical]]
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
| Mechanical model for macromolecules
|-  
|-  


| Paper
| Paper
| [[2023Wang_Autoencoder]]
| [[2016Rawson_Movement]]
| Discrete heterogeneity based on autoencoders
| Movement and flexibility
|-  
|-  


| Paper
| Paper
| [[2024Chen_Focused]]
| [[2016Shan_Multibody]]
| Focused reconstruction of heterogeneous macromolecules
| Multibody refinement
|-  
|-  


| Paper
| Paper
| [[2024Yoshidome_4D]]
| [[2016Sorzano_StructMap]]
| Heterogeneity analysis using molecular dynamics
| Sorting a discrete set of conformational states
|-  
|-  


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


| Paper
| Paper
| [[2008Stagg_TestBed]]
| [[2017Punjani_CryoSPARC]]
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
| CryoSPARC
|-  
|-  


| Paper
| Paper
| [[2011Henderson]]
| [[2017Schillbach_Warpcraft]]
| Tilt Validation
| Warpcraft: 3D Reconstruction in the presence of continuous heterogeneity
|-  
|-  


| Paper
| Paper
| [[2011Read]]
| [[2018Anden_Covariance]]
| Validation of PDBs
| Structural Variability from Noisy Tomographic Projections
|-  
|-  


| Paper
| Paper
| [[2012Henderson]]
| [[2018Haselbach_FreeEnergy]]
| EM Map Validation
| Analysis of the free energy landscape through PCA
|-  
|-  


| Paper
| Paper
| [[2013Cossio_Bayesian]]
| [[2018Nakane_MultiBody]]
| EM Map Validation in a probabilistic setting
| Structural Variability through multi-body refinement
|-  
|-  


| Paper
| Paper
| [[2013Chen_NoiseSubstitution]]
| [[2019Serna_Review]]
| Noise substitution at high resolution for measuring overfitting
| Review of classification tools
|-  
|-  


| Paper
| Paper
| [[2013Ludtke_Validation]]
| [[2018Solernou_FFEA]]
| Structural validation, example of the Calcium release channel
| Fluctuating Finite Element Analysis, continuum approach to Molecular Dynamics
|-  
|-  


| Paper
| Paper
| [[2013Murray_Validation]]
| [[2019Sorzano_Review]]
| Validation of a 3DEM structure through a particular example
| Review of continuous heterogeneity biophysics
|-  
|-  


| Paper
| Paper
| [[2014Russo_StatisticalSignificance]]
| [[2019Zhang_Local]]
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
| Local variability and covariance
|-  
|-  


| Paper
| Paper
| [[2014Stagg_Reslog]]
| [[2020Dashti_Landscape]]
| EM Map Validation through the resolution evolution with the number of particles
| Retrieving functional pathways from single particle snapshots
|-  
|-  


| Paper
| Conference
| [[2014Wasilewski_Tilt]]
| [[2020Gupta_MultiCryoGAN]]
| Web implementation of the tilt pair validation
| Reconstruction of continuously heterogeneous structures with adversarial networks
|-  
|-  


| Paper
| Paper
| [[2015Heymann_Alignability]]
| [[2020Harastani_NMA]]
| EM Map Validation through the resolution of reconstructions from particles and noise
| HEMNMA in Scipion : Using HEMNMA for analyzing continuous heterogeneity with normal modes
|-  
|-  


| Paper
| Paper
| [[2015Oliveira_FreqLimited]]
| [[2020Maji_Propagation]]
| Comparison of gold standard and frequency limited optimization
| Propagation of conformational coordinates across angular space
|-  
|-  


| Paper
| Paper
| [[2015Rosenthal_Review]]
| [[2020Moscovich_DiffusionMaps]]
| Review of validation methods
| Heterogeneity analysis by diffusion maps and spectral volumes
|-  
|-  


| Paper
| Paper
| [[2015Wriggers_Secondary]]
| [[2020Seitz_Polaris]]
| Validation by secondary structure
| Analysis of energy landscapes to find minimal action paths
|-  
|-  


| Paper
| Conference
| [[2016Degiacomi_IM]]
| [[2020Zhong_CryoDRGN]]
| Comparison of Ion Mobility data and EM volumes
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-  
|-  


| Paper
| Paper
| [[2016Kim_SAXS]]
| [[2020Verbeke_Separation]]
| Comparison of SAXS data and EM projections
| Heterogeneity analysis by comparing common lines
|-  
|-  


| Paper
| Paper
| [[2016Rosenthal_Review]]
| [[2021Chen_GM]]
| Review of validation methods
| Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability
|-  
|-  


| Paper
| Paper
| [[2016Vargas_Alignability]]
| [[2021Giraldo_cryoBIFE]]
| Validation by studying the tendency of an angular assignment to cluster in the projection space
| A Bayesian approach to extracting free‑energy profiles
|-  
|-  


| Paper
| Conference
| [[2017Monroe_PDBRefinement]]
| [[2021Hamitouche_NMADL]]
| Validation by comparison to a refined PDB
| Continuous heterogeneity analysis through normal modes and deep learning
|-  
|-  


| Paper
| Paper
| [[2018Afonine_Phenix]]
| [[2021Herreros_Zernikes3D]]
| Tools in Phenix for the validation of EM maps
| Continuous heterogeneity analysis through Zernikes 3D
|-  
|-  


| Paper
| Paper
| [[2018Heymann_Bsoft]]
| [[2021Kazemi_Enrich]]
| Map validation using Bsoft
| ENRICH: A fast method to improve the quality of flexible macromolecular reconstructions
|-  
|-  


| Paper
| Paper
| [[2018Heymann_Challenge]]
| [[2021Matsumoto_DEFmap]]
| A summary of the assessments of the 3D Map Challenge
| Prediction of RMSF of Molecular Dynamics from a CryoEM map using deep learning
|-  
|-  


| Paper
| Chapter
| [[2018Jonic_Gaussian]]
| [[2021Nakasako_Landscape]]
| Assessment of sets of volumes by pseudoatomic structures
| Estimation of free-energy landscape from images
|-  
|-  


| Paper
| Paper
| [[2018Naydenova_AngularDistribution]]
| [[2021Punjani_3DVA]]
| Evaluating the angular distribution of a 3D reconstruction
| 3D Variability analysis from images
|-  
|-  


| Paper
| Paper
| [[2018Pages_Symmetry]]
| [[2021Sorzano_PCA]]
| Looking for a symmetry axis in a PDB
| PCA is limited to low-resolution
|-  
|-  


| Paper
| Paper
| [[2018Pintilie_SSE]]
| [[2021Zhong_CryoDRGN]]
| Evaluating the quality of SSE and side chains
| CryoDRGN to analyze the continuous heterogeneity by CryoEM
|-  
|-  


| Paper
| Paper
| [[2019Herzik_Multimodel]]
| [[2022Arnold_liganded]]
| Local and global quality by multi-model fitting
| Test to see if liganded states can be detected
|-  
|-  


| Paper
| Paper
| [[2020Chen_Atomic]]
| [[2022Ecoffet_MorphOT]]
| Validation of the atomic models derived from CryoEM
| More physically plausible morphing between two states
|-  
|-  


| Paper
| Paper
| [[2020Cossio_CrossValidation]]
| [[2022Gomez_Hierarchical]]
| Need for cross validation
| Hierarchical classification of particles
|-  
|-  


| Paper
| Paper
| [[2020Ortiz_CrossValidation]]
| [[2022Hamitouche_DeepHEMNMA]]
| Cross validation for SPA
| DeepHEMNMA: Continuous heterogeneity analysis through normal modes and deep learning
|-
 
| Conference
| [[2022Levy_CryoFire]]
| CryoFire: heterogeneity and alignment through amortized inference
|-  
|-  


| Paper
| Paper
| [[2020Sazzed_helices]]
| [[2022Rabuck_Quant]]
| Validation of helix quality
| Workflow for discrete heterogeneity analysis
|-  
|-  


| Paper
| Paper
| [[2020Stojkovic_PTM]]
| [[2022Seitz_ESPER]]
| Validation of post-translational modifications
| ESPER through manifold embeddings
|-  
|-  


| Paper
| Paper
| [[2020Tiwari_PixelSize]]
| [[2022Skalidis_Endogenous]]
| Fine determination of the pixel size
| AI tools to recognize proteins in cellular fractions
|-  
|-  


| Paper
| Paper
| [[2021Mendez_Graph]]
| [[2022Wu_Manifold]]
| Identification of incorrectly oriented particles
| Continuous heterogeneity through manifold learning
|-  
|-  


| Paper
| Paper
| [[2021Pintilie_Validation]]
| [[2022Zhou_Data]]
| Review of map validation approaches
| Determination of the number of discrete 3D classes
|-  
|-  


| Paper
| Paper
| [[2021Olek_FDR]]
| [[2023Barchet_Focused]]
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
| Applications and strategies in focused classification and refinement
|-  
|-  


| Paper
| Paper
| [[2022Garcia_DeepHand]]
| [[2023Afonine_Varref]]
| Checking the correct handedness with a neural network
| Phenix.varref for the analysis of the model heterogeneity
|-  
|-  


| Paper
| Paper
| [[2022Sorzano_Bias]]
| [[2023Chen_GMM]]
| Bias, variance, gold-standard and overfitting in SPA
| Continuous heterogeneity analysis with GMMs and neural networks
|-  
|-  


| Paper
| Paper
| [[2022Sorzano_Validation]]
| [[2023Dsouza_benchmark]]
| Validation scheme and server for SPA
| Benchmark analysis of various continuous heterogeneity algorithms
|-  
|-  


| Paper
| Paper
| [[2022Terashi_DAQ]]
| [[2023Esteve_Spectral]]
| Validation of models fitted into CryoEM maps
| Continuous heterogeneity analysis through the spectral decomposition of the atomic structure
|-  
|-  


| Paper
| Paper
| [[2022Waarshamanage_EMDA]]
| [[2023Fernandez_Subtraction]]
| Validation of models fitted into CryoEM maps
| Subtraction of unwanted signals to improve classification and alignment
|-  
|-  


| Paper
| Paper
| [[2024Feng_DeepQs]]
| [[2023Forsberg_Filter]]
| DeepQ: Local quality of the map
| Filter to estimate the local heterogeneity
|-  
|-  


| Paper
| Paper
| [[2024Verbeke_SelfFSC]]
| [[2023Herreros_Hub]]
| Self FSC: FSC with a single map
| Flexibility hub: an integrative platform for continuous heterogeneity
|-  
|-  


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


| Paper
| Paper
| [[1986Harauz_FBP]]
| [[2023Kinman_Analysis]]
| Fourier Shell Correlation
| Analysis of the continuous heterogeneity results of CryoDrgn
|-  
|-  


| Paper
| Paper
| [[1987Unser_SSNR]]
| [[2023Matsumoto_DEFmap]]
| 2D Spectral Signal to Noise Ratio
| Quantitative analysis of the prediction of RMSF from a map using DefMap
|-  
|-  


| Paper
| Paper
| [[2002Penczek_SSNR]]
| [[2023Punjani_3DFlex]]
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
| Continuous heterogeneity through 3DFlex
|-  
|-  


| Paper
| Paper
| [[2003Rosenthal_DPR]]
| [[2023Seitz_Geometric]]
| Review of the FSC and establishment of a new threshold
| Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections
|-  
|-  


| Paper
| Paper
| [[2005Unser_SSNR]]
| [[2023Seitz_ESPER]]
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
| Continuous heterogeneity through Embedded subspace partitioning and eigenfunction realignment
|-  
|-  


| Paper
| Paper
| [[2005VanHeel_FSC]]
| [[2023Tang_Reweighting]]
| Establishment of a new threshold for FSC
| Ensemble reweighting using Cryo-EM particles
|-  
|-  


| Paper
| Paper
| [[2007Sousa_AbInitio]]
| [[2023Vuillemot_MDSPACE]]
| Resolution measurement on neighbour Fourier voxels
| MDSPACE: Continuous heterogeneity analysis through normal modes and MD simulation
|-  
|-  


| Paper
| Paper
| [[2014Kucukelbir_Local]]
| [[2023Wang_Autoencoder]]
| Quantifying the local resolution of cryo-EM density maps
| Discrete heterogeneity based on autoencoders
|-  
|-  


| Paper
| Paper
| [[2016Pintilie_Probabilistic]]
| [[2024Amisaki_Multilevel]]
| Probabilistic models and resolution
| Multilevel PCA for the analysis of hierarchical continuous heterogeneity
|-  
|-  


| Paper
| Paper
| [[2017Sorzano_FourierProperties]]
| [[2024Chen_Focused]]
| Statistical properties of resolution measures defined in Fourier space
| Focused reconstruction of heterogeneous macromolecules
|-  
|-  


| Conference
| Paper
| [[2018Avramov_DeepLearning]]
| [[2024Fan_CryoTrans]]
| Deep learning classification of volumes into low, medium and high resolution
| CryoTrans: Trajectory generation between two states
|-  
|-  


| Paper
| Paper
| [[2018Carugo_BFactors]]
| [[2024Klindt_Disentanglement]]
| How large can B-factors be in protein crystals
| Disentanglement of pose and conformation in the latent space of heterogeneity analysis algorithms
|-  
|-  


| Conference
| Conference
| [[2018Kim_FourierError]]
| [[2024Levy_Hydra]]
| Comparison between a gold standard and a reconstruction
| Hydra: Continuous and discrete heterogeneity using neural fields
|-  
|-  


| Paper
| Paper
| [[2018Rupp_Problems]]
| [[2024Li_CryoStar]]
| Problems of resolution as a proxy number for map quality
| CryoStar: Continuous heterogeneity analysis with structural priors
|-  
|-  


| Paper
| Paper
| [[2018Vilas_MonoRes]]
| [[2024Schwab_DynaMight]]
| Local resolution by monogenic signals
| DynaMight: Heterogeneity analysis using neural networks
|-  
|-  


| Paper
| Paper
| [[2018Yang_Multiscale]]
| [[2024Shi_Priors]]
| Resolution from a multiscale spectral analysis
| Latent space priors for continuous heterogeneity
|-  
|-  


| Paper
| Paper
| [[2019Avramov_DeepLearning]]
| [[2024Song_RMSFNet]]
| Deep learning classification of volumes into low, medium and high resolution
| RMSFNet: prediction of Molecular Dynamics RMSF from the cryoEM map
|-  
|-  


| Paper
| Paper
| [[2019Heymann_Statistics]]
| [[2024Yoshidome_4D]]
| SNR, FSC, and related statistics
| Heterogeneity analysis using molecular dynamics
|-  
|-  


| Paper
| Paper
| [[2019Ramirez_DeepRes]]
| [[2025Chen_GMM]]
| Resolution determination by deep learning
| Continuous heterogeneity analysis in SPA using atomic models
|-  
|-  


| Paper
| Paper
| [[2020Baldwin_Lyumkis_SCF]]
| [[2025Dingeldein]]
| Resolution attenuation through non-uniform Fourier sampling
| Amortized template matching using simulation-based inference
|-  
|-  


| Paper
| Paper
| [[2020Beckers_Permutation]]
| [[2025Herreros_HetSiren]]
| Permutation tests for the FSC
| Discrete and Continuous heterogeneity analysis using neural networks
|-  
|-  


| Paper
| Paper
| [[2020Penczek_mFSC]]
| [[2025Gilles_Covariance]]
| Modified FSC to avoid mask induced artifacts
| Continuous heterogeneity analysis using regularized covariance estimation and kernel regression
|-  
|-  


| Paper
| Paper
| [[2020Vilas_MonoDir]]
| [[2025Kinman_SIREN]]
| Local and directional resolution
| Heterogeneity analysis using coocurrence analysis (SIREN)
|-  
|-  


| Paper
| Paper
| [[2023Dai_CryoRes]]
| [[2025Lauzirika_Distinguishable]]
| Local resolution through deep learning
| How many (distinguishable) classes can we identify in single-particle analysis?
|-  
|-  


| Paper
| Paper
| [[2023Vilas_FSO]]
| [[2025Levy_CryoDRGNAI]]
| Fourier Shell Occupancy to measure anisotropy
| CryoDRGN-AI: Heterogeneity analysis and ab initio 3D reconstruction for SPA and STA
|-  
|-  


|}
|}


=== Sharpening of high resolution information ===
=== Validation ===
 
{|
{|
 
| Paper
| Paper
| [[2003Rosenthal_DPR]]
| [[2008Stagg_TestBed]]
| Contrast restoration and map sharpening
| Effect of voltage, dosis, number of particles and Euler jumps on resolution
|-  
|-  


| Paper
| Paper
| [[2008Fernandez_Bfactor]]
| [[2011Henderson]]
| Bfactor determination and restoration
| Tilt Validation
|-  
|-  


| Paper
| Paper
| [[2013Fiddy_SaxtonAlgorithm]]
| [[2011Read]]
| Phase retrieval or extension
| Validation of PDBs
|-  
|-  


| Paper
| Paper
| [[2014Kishchenko_SphericalDeconvolution]]
| [[2012Henderson]]
| Spherical deconvolution
| EM Map Validation
|-  
|-  


| Paper
| Paper
| [[2015Spiegel_VISDEM]]
| [[2013Cossio_Bayesian]]
| Visualization improvement by the use of pseudoatomic profiles
| EM Map Validation in a probabilistic setting
|-  
|-  


| Paper
| Paper
| [[2016Jonic_Pseudoatoms]]
| [[2013Chen_NoiseSubstitution]]
| Approximation with pseudoatoms
| Noise substitution at high resolution for measuring overfitting
|-  
|-  


| Paper
| Paper
| [[2016Jonic_Denoising]]
| [[2013Ludtke_Validation]]
| Denoising and high-frequency boosting by pseudoatom approximation
| Structural validation, example of the Calcium release channel
|-  
|-  


| Paper
| Paper
| [[2017Jakobi_LocScale]]
| [[2013Murray_Validation]]
| Sharpening based on an atomic model
| Validation of a 3DEM structure through a particular example
|-  
|-  


| Paper
| Paper
| [[2019Ramlaul_Filtering]]
| [[2014Russo_StatisticalSignificance]]
| Local agreement filtering (denoising)
| EM Map Validation through the statistical significance of the tilt-pair angular assignment
|-  
|-  


| Conference
| Paper
| [[2020Mullick_SuperResolution]]
| [[2014Stagg_Reslog]]
| Superresolution from a map
| EM Map Validation through the resolution evolution with the number of particles
|-  
|-  


| Paper
| Paper
| [[2020Ramirez_LocalDeblur]]
| [[2014Wasilewski_Tilt]]
| Local deblur (local Wiener filter)
| Web implementation of the tilt pair validation
|-  
|-  


| Paper
| Paper
| [[2020Terwilliger_density]]
| [[2015Heymann_Alignability]]
| Density modification of CryoEM maps
| EM Map Validation through the resolution of reconstructions from particles and noise
|-  
|-  


| Paper
| Paper
| [[2020Vilas_Bfactor]]
| [[2015Oliveira_FreqLimited]]
| Global B-factor correction does not represent macromolecules
| Comparison of gold standard and frequency limited optimization
|-  
|-  


| Paper
| Paper
| [[2021Beckers_Interpretation]]
| [[2015Rosenthal_Review]]
| Improvements from the raw reconstruction to a structure to model
| Review of validation methods
|-  
|-  


| Paper
| Paper
| [[2021Kaur_LocSpiral]]
| [[2015Wriggers_Secondary]]
| LocSpiral, LocBsharpen, LocBfactor
| Validation by secondary structure
|-  
|-  


| Paper
| Paper
| [[2021Fernandez_Adjustment]]
| [[2016Degiacomi_IM]]
| Map adjustment for subtraction, consensus and sharpening
| Comparison of Ion Mobility data and EM volumes
|-  
|-  


| Paper
| Paper
| [[2021Sanchez_DeepEMhancer]]
| [[2016Kim_SAXS]]
| Deep learning algorithm for volume restoration
| Comparison of SAXS data and EM projections
|-  
|-  


| Paper
| Paper
| [[2022Gilles_Wilson]]
| [[2016Rosenthal_Review]]
| A molecular prior distribution for Bayesian inference based on Wilson statistics
| Review of validation methods
|-  
|-  


| Paper
| Paper
| [[2022Vargas_tubular]]
| [[2016Vargas_Alignability]]
| Map enhancement by multiscale tubular filter
| Validation by studying the tendency of an angular assignment to cluster in the projection space
|-  
|-  


| Paper
| Paper
| [[2023He_EMReady]]
| [[2017Monroe_PDBRefinement]]
| Map enhancement with local and non-local deep learning (EMReady)
| Validation by comparison to a refined PDB
|-  
|-  


| Paper
| Paper
| [[2023Maddhuri_EMGan]]
| [[2018Afonine_Phenix]]
| Map enhancement with GANs (EMGan)
| Tools in Phenix for the validation of EM maps
|-  
|-  


|}
| Paper
 
| [[2018Heymann_Bsoft]]
=== CTF estimation and restoration ===
| Map validation using Bsoft
 
|-
{|


| Paper
| Paper
| [[1982Schiske_Correction]]
| [[2018Heymann_Challenge]]
| CTF correction for tilted objects
| A summary of the assessments of the 3D Map Challenge
|-  
|-  


| Paper
| Paper
| [[1988Toyoshima_Model]]
| [[2018Jonic_Gaussian]]
| CTF estimation
| Assessment of sets of volumes by pseudoatomic structures
|-  
|-  


| Paper
| Paper
| [[1995Frank_Wiener]]
| [[2018Naydenova_AngularDistribution]]
| CTF correction using Wiener filter
| Evaluating the angular distribution of a 3D reconstruction
|-  
|-  


| Paper
| Paper
| [[1996Skoglund_MaxEnt]]
| [[2018Pages_Symmetry]]
| CTF correction with Maximum Entropy
| Looking for a symmetry axis in a PDB
|-  
|-  


| Paper
| Paper
| [[1996Zhou_Model]]
| [[2018Pintilie_SSE]]
| CTF model and user interface for manual fitting
| Evaluating the quality of SSE and side chains
|-  
|-  


| Paper
| Paper
| [[1997Fernandez_AR]]
| [[2019Herzik_Multimodel]]
| PSD estimation using periodogram averaging and AR models
| Local and global quality by multi-model fitting
|-  
|-  


| Paper
| Paper
| [[1997Penczek_Wiener]]
| [[2020Chen_Atomic]]
| CTF correction using Wiener filter
| Validation of the atomic models derived from CryoEM
|-  
|-  


| Paper
| Paper
| [[1997Stark_Deconvolution]]
| [[2020Cossio_CrossValidation]]
| CTF correction using deconvolution
| Need for cross validation
|-  
|-  


| Paper
| Paper
| [[1997Zhu_RecCTF]]
| [[2020Ortiz_CrossValidation]]
| CTF correction and reconstruction
| Cross validation for SPA
|-  
|-  


| Paper
| Paper
| [[2000DeRosier_EwaldCorrection]]
| [[2020Sazzed_helices]]
| CTF correction considering the Ewald sphere
| Validation of helix quality
|-  
|-  


| Paper
| Paper
| [[2000Jensen_TiltedCorrection]]
| [[2020Stojkovic_PTM]]
| CTF correction considering tilt in backprojection
| Validation of post-translational modifications
|-  
|-  


| Paper
| Paper
| [[2001Saad_CTFEstimate]]
| [[2020Tiwari_PixelSize]]
| CTF estimation
| Fine determination of the pixel size
|-  
|-  


| Paper
| Paper
| [[2003Huang_CTFEstimate]]
| [[2021Mendez_Graph]]
| CTF estimation
| Identification of incorrectly oriented particles
|-  
|-  


| Paper
| Paper
| [[2003Mindell_CTFTILT]]
| [[2021Pintilie_Validation]]
| CTF estimation for tilted micrographs
| Review of map validation approaches
|-  
|-  


| Paper
| Paper
| [[2003Sander_MSA]]
| [[2021Olek_FDR]]
| CTF estimation through MSA classification of PSDs
| Cryo-EM Map–Based Model Validation Using the False Discovery Rate Approach
|-  
|-  


| Paper
| Paper
| [[2003Velazquez_ARMA]]
| [[2022Garcia_DeepHand]]
| PSD and CTF estimation using ARMA models
| Checking the correct handedness with a neural network
|-  
|-  


| Paper
| Paper
| [[2004Sorzano_IDR]]
| [[2022Sorzano_Bias]]
| CTF restoration and reconstruction with Iterative Data Refinement
| Bias, variance, gold-standard and overfitting in SPA
|-  
|-  


| Conference
| Paper
| [[2004Wan_CTF]]
| [[2022Sorzano_Validation]]
| Spatially variant CTF
| Validation scheme and server for SPA
|-  
|-  


| Paper
| Paper
| [[2004Zubelli_Chahine]]
| [[2022Terashi_DAQ]]
| CTF restoration and reconstruction with Chahine's multiplicative method
| Validation of models fitted into CryoEM maps
|-
 
| Conference
| [[2005Dubowy_SpaceVariant]]
| CTF correction when this is space variant
|-  
|-  


| Paper
| Paper
| [[2005Mallick_ACE]]
| [[2022Waarshamanage_EMDA]]
| CTF estimation
| Validation of models fitted into CryoEM maps
|-  
|-  


| Paper
| Paper
| [[2006Wolf_Ewald]]
| [[2024Feng_DeepQs]]
| CTF correction considering Ewald sphere
| DeepQ: Local quality of the map
|-  
|-  


| Paper
| Paper
| [[2007Jonic_EnhancedPSD]]
| [[2024Jeon_CryoBench]]
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
| Datasets for heterogeneity benchmarking
|-  
|-  


| Paper
| Paper
| [[2007Philippsen_Model]]
| [[2024Lytje_SAXS]]
| CTF Model for tilted specimens
| Validation of CryoEM maps with SAXS curves
|-  
|-  


| Paper
| Paper
| [[2007Sorzano_CTF]]
| [[2024Sanchez_Anisotropy]]
| CTF estimation using enhanced PSDs
| New measure of anisotropy in maps
|-  
|-  


| Paper
| Paper
| [[2009Sorzano_Sensitivity]]
| [[2024Verbeke_SelfFSC]]
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
| Self FSC: FSC with a single map
|-  
|-  


| Paper
| Paper
| [[2010Jiang2010_CTFCorrection]]
| [[2025Bromberg_Hand]]
| Amplitude correction method
| Handedness validation based on the Ewald sphere
|-  
|-  


| Paper
| Paper
| [[2010Kasantsev_CTFCorrection]]
| [[2025Pintilie_QScore]]
| Mathematical foundations of Kornberg and Jensen method
| Extension of Q-Score to analyze SPA maps
|-  
|-  
|}
=== Resolution ===
{|


| Paper
| Paper
| [[2010Leong_CTFCorrection]]
| [[1986Harauz_FBP]]
| Correction for spatially variant CTF
| Fourier Shell Correlation
|-  
|-  


| Paper
| Paper
| [[2011Glaeser_Coma]]
| [[1987Unser_SSNR]]
| The effect of coma at high-resolution
| 2D Spectral Signal to Noise Ratio
|-  
|-  


| Paper
| Paper
| [[2011Mariani_Tilted]]
| [[2002Penczek_SSNR]]
| CTF simulation and correction of tilted specimens
| 3D Spectral Signal to Noise Ratio for Fourier based algorithms
|-  
|-  


| Paper
| Paper
| [[2011Sindelar_Wiener]]
| [[2003Rosenthal_DPR]]
| CTF correction using a modified version of Wiener filter
| Review of the FSC and establishment of a new threshold
|-  
|-  


| Paper
| Paper
| [[2011Voortman_Tilted]]
| [[2005Unser_SSNR]]
| CTF correction for tilted specimen
| 3D Spectral Signal to Noise Ratio for any kind of algorithms
|-  
|-  


| Paper
| Paper
| [[2012Voortman_VaryingCTF]]
| [[2005VanHeel_FSC]]
| Correcting a spatially varying CTF
| Establishment of a new threshold for FSC
|-  
|-  


| Paper
| Paper
| [[2013Vargas_FastDef]]
| [[2007Sousa_AbInitio]]
| Fast defocus
| Resolution measurement on neighbour Fourier voxels
|-  
|-  


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


| Paper
| Paper
| [[2015Rohou_CTFFind4]]
| [[2016Pintilie_Probabilistic]]
| CTF Find 4
| Probabilistic models and resolution
|-  
|-  


| Paper
| Paper
| [[2015Sheth_CTFquality]]
| [[2017Sorzano_FourierProperties]]
| Visualization and quality assessment of CTF
| Statistical properties of resolution measures defined in Fourier space
|-  
|-  


| Paper
| Conference
| [[2016Zhang_GCTF]]
| [[2018Avramov_DeepLearning]]
| gCTF
| Deep learning classification of volumes into low, medium and high resolution
|-  
|-  


| Paper
| Paper
| [[2018Su_GoCTF]]
| [[2018Carugo_BFactors]]
| goCTF, CTF for tilted specimens
| How large can B-factors be in protein crystals
|-  
|-  


| Paper
| Conference
| [[2020Heimowitz_Aspire]]
| [[2018Kim_FourierError]]
| CTF determination in Aspire
| Comparison between a gold standard and a reconstruction
|-  
|-  


| Paper
| Paper
| [[2020Zivanov_HighOrder]]
| [[2018Rupp_Problems]]
| Estimation of high order aberrations
| Problems of resolution as a proxy number for map quality
|-  
|-  


| Paper
| Paper
| [[2023Fernandez_Local]]
| [[2018Vilas_MonoRes]]
| Local defocus estimation
| Local resolution by monogenic signals
|-  
|-  
|}
=== Segmentation ===
{|


| Paper
| Paper
| [[2006Baker_segmentation]]
| [[2018Yang_Multiscale]]
| Segmentation of molecular subunits
| Resolution from a multiscale spectral analysis
|-  
|-  


| Paper
| Paper
| [[2010Pintilie_segger]]
| [[2019Avramov_DeepLearning]]
| Segmentation of molecular subunits
| Deep learning classification of volumes into low, medium and high resolution
|-  
|-  


| Conference
| Paper
| [[2017Nissenson_VolumeCut]]
| [[2019Heymann_Statistics]]
| Segmentation of an EM volume using an atomic model
| SNR, FSC, and related statistics
|-  
|-  


| Paper
| Paper
| [[2019Beckers_FDR]]
| [[2019Ramirez_DeepRes]]
| Segmentation of the protein using False Discovery Rate
| Resolution determination by deep learning
|-  
|-  


| Paper
| Paper
| [[2020Beckers_FDR]]
| [[2020Baldwin_Lyumkis_SCF]]
| Segmentation of the protein using False Discovery Rate (GUI)
| Resolution attenuation through non-uniform Fourier sampling
|-  
|-  


| Paper
| Paper
| [[2020Farkas_MemBlob]]
| [[2020Beckers_Permutation]]
| Segmentation of membrane in membrane embedded proteins
| Permutation tests for the FSC
|-  
|-  


| Paper
| Paper
| [[2020Terashi_MainMastSeg]]
| [[2020Penczek_mFSC]]
| Segmentation of proteins into domains
| Modified FSC to avoid mask induced artifacts
|-  
|-  


| Paper
| Paper
| [[2021He_EMNUSS]]
| [[2020Vilas_MonoDir]]
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
| Local and directional resolution
|-  
|-  
|}
=== Fitting and docking ===
{|


| Paper
| Paper
| [[1999Volkmann_Fitting]]
| [[2023Dai_CryoRes]]
| Fitting in real space
| Local resolution through deep learning
|-  
|-  


| Paper
| Paper
| [[2001Baker_Review]]
| [[2023Vilas_FSO]]
| Review of protein structure prediction
| Fourier Shell Occupancy to measure anisotropy
|-  
|-  


| Paper
| Paper
| [[2001Jones_Review]]
| [[2025Urzhumtsev_RescaleFSC]]
| Review of protein structure prediction
| Rescaling of the FSC
|-  
|-  


|}
=== Sharpening of high resolution information ===
{|
| Paper
| Paper
| [[2003Kovacs_FRM3D]]
| [[2003Rosenthal_DPR]]
| Fast Rotational Alignment of two EM maps
| Contrast restoration and map sharpening
|-  
|-  


| Paper
| Paper
| [[2004Tama_NMA1]]
| [[2008Fernandez_Bfactor]]
| Flexible fitting with Normal Modes (I)
| Bfactor determination and restoration
|-  
|-  


| Paper
| Paper
| [[2004Tama_NMA2]]
| [[2013Fiddy_SaxtonAlgorithm]]
| Flexible fitting with Normal Modes (II)
| Phase retrieval or extension
|-  
|-  


| Paper
| Paper
| [[2005Velazquez_Superfamilies]]
| [[2014Kishchenko_SphericalDeconvolution]]
| Recognition of the superfamily folding in medium-high resolution volumes
| Spherical deconvolution
|-  
|-  


| Paper
| Paper
| [[2007DeVries_Haddock]]
| [[2015Spiegel_VISDEM]]
| Docking with Haddock 2.0
| Visualization improvement by the use of pseudoatomic profiles
|-  
|-  


| Paper
| Paper
| [[2007Kleywegt_QualityControl]]
| [[2016Jonic_Pseudoatoms]]
| Quality control and validation of fitting
| Approximation with pseudoatoms
|-  
|-  


| Paper
| Paper
| [[2008Orzechowski_Flexible]]
| [[2016Jonic_Denoising]]
| Flexible fitting with biased molecular dynamics
| Denoising and high-frequency boosting by pseudoatom approximation
|-  
|-  


| Paper
| Paper
| [[2008Rusu_Interpolation]]
| [[2017Jakobi_LocScale]]
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
| Sharpening based on an atomic model
|-  
|-  


| Paper
| Paper
| [[2012Biswas_Secondary]]
| [[2019Ramlaul_Filtering]]
| Secondary structure determination in EM volumes
| Local agreement filtering (denoising)
|-  
|-  


| Paper
| Conference
| [[2012Velazquez_Constraints]]
| [[2020Mullick_SuperResolution]]
| Multicomponent fitting by using constraints from other information sources
| Superresolution from a map
|-  
|-  


| Paper
| Paper
| [[2013Chapman MS_Atomicmodeling]]
| [[2020Ramirez_LocalDeblur]]
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
| Local deblur (local Wiener filter)
|-  
|-  


| Paper
| Paper
| [[2013Esquivel_Modelling]]
| [[2020Terwilliger_density]]
| Review on modelling (secondary structure, fitting, ...)
| Density modification of CryoEM maps
|-  
|-  


| Paper
| Paper
| [[2013Lopez_Imodfit]]
| [[2020Vilas_Bfactor]]
| Fitting based on vibrational analysis
| Global B-factor correction does not represent macromolecules
|-  
|-  


| Paper
| Paper
| [[2013Nogales_3DEMLoupe]]
| [[2021Beckers_Interpretation]]
| Normal Mode Analysis of reconstructed volumes
| Improvements from the raw reconstruction to a structure to model
|-  
|-  


| Paper
| Paper
| [[2014AlNasr_Secondary]]
| [[2021Kaur_LocSpiral]]
| Identification of secondary structure elements in EM volumes
| LocSpiral, LocBsharpen, LocBfactor
|-  
|-  


| Paper
| Paper
| [[2014Politis_MassSpect]]
| [[2021Fernandez_Adjustment]]
| Integration of mass spectroscopy information
| Map adjustment for subtraction, consensus and sharpening
|-  
|-  


| Paper
| Paper
| [[2014Rey_MassSpect]]
| [[2021Sanchez_DeepEMhancer]]
| Integration of mass spectroscopy information
| Deep learning algorithm for volume restoration
|-  
|-  


| Paper
| Paper
| [[2014Villa_Review]]
| [[2022Gilles_Wilson]]
| Review of atomic fitting into EM volumes
| A molecular prior distribution for Bayesian inference based on Wilson statistics
|-  
|-  


| Paper
| Paper
| [[2015Barad_EMRinger]]
| [[2022Vargas_tubular]]
| Validation of hybrid models
| Map enhancement by multiscale tubular filter
|-  
|-  


| Paper
| Paper
| [[2015Bettadapura_PF2Fit]]
| [[2023He_EMReady]]
| Fast rigid fitting of PDBs into EM maps
| Map enhancement with local and non-local deep learning (EMReady)
|-  
|-  


| Paper
| Paper
| [[2015Carrillo_CapsidMaps]]
| [[2023Maddhuri_EMGan]]
| Analysis of virus capsids using Google Maps
| Map enhancement with GANs (EMGan)
|-  
|-  


| Paper
| Paper
| [[2015Hanson_Continuum]]
| [[2024Agarwal_crefDenoiser]]
| Modelling assemblies with continuum mechanics
| cRefDenoiser: map denoising based on deep learning
|-  
|-  


| Paper
| Paper
| [[2015Lopez_Review]]
| [[2024Kimanius_Blush]]
| Review of structural modelling from EM data
| Blush: data-driven regularization
|-  
|-  


| Paper
| Paper
| [[2015Schroeder_Hybrid]]
| [[2025Selvaraj_CryoTEN]]
| Review on model building
| CryoTEN: map enhancement using transformers
|-  
|-  
|}
=== CTF estimation and restoration ===
{|


| Paper
| Paper
| [[2015Tamo_Dynamics]]
| [[1982Schiske_Correction]]
| Dynamics in integrative modeling
| CTF correction for tilted objects
|-  
|-  


| Paper
| Paper
| [[2015Sorzano_AtomsToVoxels]]
| [[1988Toyoshima_Model]]
| Accurate conversion of an atomic model into a voxel density volume
| CTF estimation
|-  
|-  


| Paper
| Paper
| [[2016Joseph_Evolution]]
| [[1995Frank_Wiener]]
| Evolutionary constraints for the fitting of atomic models into density maps
| CTF correction using Wiener filter
|-  
|-  


| Paper
| Paper
| [[2016Joseph_Refinement]]
| [[1996Skoglund_MaxEnt]]
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
| CTF correction with Maximum Entropy
|-  
|-  


| Paper
| Paper
| [[2016Murshudov_Refinement]]
| [[1996Zhou_Model]]
| Refinement of atomic models in high-resolution EM reconstructions
| CTF model and user interface for manual fitting
|-  
|-  


| Paper
| Paper
| [[2016Segura_3Diana]]
| [[1997Fernandez_AR]]
| Validation of hybrid models
| PSD estimation using periodogram averaging and AR models
|-  
|-  


| Paper
| Paper
| [[2016Singharoy_MDFF]]
| [[1997Penczek_Wiener]]
| Construction of hybrid models driven by EM density and molecular dynamics
| CTF correction using Wiener filter
|-  
|-  


| Paper
| Paper
| [[2016Wang_Rosetta]]
| [[1997Stark_Deconvolution]]
| Construction of hybrid models driven by EM density using Rosetta
| CTF correction using deconvolution
|-  
|-  


| Paper
| Paper
| [[2017Chen_CoarseGraining]]
| [[1997Zhu_RecCTF]]
| Coarse graining of EM volumes
| CTF correction and reconstruction
|-  
|-  


| Paper
| Paper
| [[2017Joseph_Metrics]]
| [[2000DeRosier_EwaldCorrection]]
| Metrics analysis for the comparison of structures
| CTF correction considering the Ewald sphere
|-  
|-  


| Paper
| Paper
| [[2017Hryc_WeightedAtoms]]
| [[2000Jensen_TiltedCorrection]]
| Construction of hybrid models by locally weighting the different atoms
| CTF correction considering tilt in backprojection
|-  
|-  


| Paper
| Paper
| [[2017Matsumoto_Distribution]]
| [[2001Saad_CTFEstimate]]
| Estimating the distribution of conformations of atomic models
| CTF estimation
|-  
|-  


| Paper
| Paper
| [[2017Michel_ContactPrediction]]
| [[2003Huang_CTFEstimate]]
| Structure prediction by contact prediction
| CTF estimation
|-  
|-  


| Paper
| Paper
| [[2017Miyashita_EnsembleFitting]]
| [[2003Mindell_CTFTILT]]
| Ensemble fitting using Molecular Dynamics
| CTF estimation for tilted micrographs
|-  
|-  


| Paper
| Paper
| [[2017Turk_ModelBuilding]]
| [[2003Sander_MSA]]
| Tutorial on model building and protein visualization
| CTF estimation through MSA classification of PSDs
|-  
|-  


| Paper
| Paper
| [[2017Wang_PartialCharges]]
| [[2003Velazquez_ARMA]]
| Appearance of partial charges in EM maps
| PSD and CTF estimation using ARMA models
|-  
|-  


| Paper
| Paper
| [[2017Wlodawer]]
| [[2004Sorzano_IDR]]
| Comparison of X-ray and EM high resolution structures
| CTF restoration and reconstruction with Iterative Data Refinement
|-  
|-  


| Paper
| Conference
| [[2018Cassidy_review]]
| [[2004Wan_CTF]]
| Review of methods for hybrid modeling
| Spatially variant CTF
|-  
|-  


| Paper
| Paper
| [[2018Chen_SudeChains]]
| [[2004Zubelli_Chahine]]
| A comparison of side chains between X-ray and EM maps
| CTF restoration and reconstruction with Chahine's multiplicative method
|-  
|-  


| Paper
| Conference
| [[2018Kawabata_Pseudoatoms]]
| [[2005Dubowy_SpaceVariant]]
| Modelling the EM map with Gaussian pseudoatoms
| CTF correction when this is space variant
|-  
|-  


| Paper
| Paper
| [[2018Kovacs_Medium]]
| [[2005Mallick_ACE]]
| Modelling of medium resolution EM maps
| CTF estimation
|-  
|-  


| Paper
| Paper
| [[2018Neumann_validation]]
| [[2006Wolf_Ewald]]
| Validation of fitting, resolution assessment and quality of fit
| CTF correction considering Ewald sphere
|-  
|-  


| Paper
| Paper
| [[2018Terwilliger_map_to_model]]
| [[2007Jonic_EnhancedPSD]]
| Phenix map_to_model, automatic modelling of EM volumes
| PSD enhancement for better identification of Thon rings; Vitreous ice diffracts in Thon rings
|-  
|-  


| Paper
| Paper
| [[2018Wang_MD]]
| [[2007Philippsen_Model]]
| Constructing atomic models using molecular dynamics
| CTF Model for tilted specimens
|-  
|-  


| Paper
| Paper
| [[2018Xia_MVPENM]]
| [[2007Sorzano_CTF]]
| Multiscale Normal Mode Analysis
| CTF estimation using enhanced PSDs
|-  
|-  


| Paper
| Paper
| [[2018Yu_Atomic]]
| [[2009Sorzano_Sensitivity]]
| Constructing atomic models using existing tools
| Error sensitivity of the CTF models, non-uniqueness of the CTF parameters
|-  
|-  


| Paper
| Paper
| [[2019Bonomi_Multiscale]]
| [[2010Jiang2010_CTFCorrection]]
| Bayesian multi-scale modelling
| Amplitude correction method
|-  
|-  


| Paper
| Paper
| [[2019Kidmose_Namdinator]]
| [[2010Kasantsev_CTFCorrection]]
| Namdinator: Flexible fitting with NAMD
| Mathematical foundations of Kornberg and Jensen method
|-  
|-  


| Paper
| Paper
| [[2019Klaholz_Review]]
| [[2010Leong_CTFCorrection]]
| Review of Phenix tools to modelling
| Correction for spatially variant CTF
|-  
|-  


| Paper
| Paper
| [[2019Subramaniya_DeepSSE]]
| [[2011Glaeser_Coma]]
| Secondary structure prediction from maps using deep learning
| The effect of coma at high-resolution
|-  
|-  


| Paper
| Paper
| [[2019Zhang_CoarseGrained]]
| [[2011Mariani_Tilted]]
| Coarse-graining of EM maps
| CTF simulation and correction of tilted specimens
|-  
|-  


| Paper
| Paper
| [[2020Costa_MDeNM]]
| [[2011Sindelar_Wiener]]
| Flexible fitting with molecular dynamics and normal modes
| CTF correction using a modified version of Wiener filter
|-  
|-  


| Paper
| Paper
| [[2020Cragnolini_Tempy2]]
| [[2011Voortman_Tilted]]
| TEMpy2 library for density-fitting and validation
| CTF correction for tilted specimen
|-  
|-  


| Paper
| Paper
| [[2020Dodd_ModelBuilding]]
| [[2012Voortman_VaryingCTF]]
| Model building possibilities, with special emphasis on flexible fitting
| Correcting a spatially varying CTF
|-  
|-  


| Paper
| Paper
| [[2020Ho_CryoID]]
| [[2013Vargas_FastDef]]
| Identification of proteins in structural proteomics from cryoEM volumes
| Fast defocus
|-  
|-  


| Paper
| Paper
| [[2020Hoh_Buccaneer]]
| [[2014Penczek_CTER]]
| Structure modelling with Buccaneer
| Estimation of the CTF errors
|-  
|-  


| Paper
| Paper
| [[2020Joseph_comparison]]
| [[2015Rohou_CTFFind4]]
| Comparison of map and model, or two maps
| CTF Find 4
|-  
|-  


| Paper
| Paper
| [[2020Kim_Review]]
| [[2015Sheth_CTFquality]]
| Review of the options for atomic modelling
| Visualization and quality assessment of CTF
|-  
|-  


| Paper
| Paper
| [[2020Leelananda_Constraints]]
| [[2016Zhang_GCTF]]
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
| gCTF
|-  
|-  


| Paper
| Paper
| [[2020Liebschner_Ceres]]
| [[2018Su_GoCTF]]
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
| goCTF, CTF for tilted specimens
|-  
|-  


| Paper
| Paper
| [[2020Oroguchi]]
| [[2020Heimowitz_Aspire]]
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
| CTF determination in Aspire
|-  
|-  


| Paper
| Paper
| [[2020Vant_Flexible]]
| [[2020Zivanov_HighOrder]]
| Flexible fitting with molecular dynamics and neural network potentials
| Estimation of high-order aberrations
|-  
|-  


| Paper
| Paper
| [[2021Behkamal_Secondary]]
| [[2022Pant_ExitWave]]
| Secondary structure from medium resolution maps
| Estimation of the electron exit-wave
|-  
|-  


| Paper
| Paper
| [[2021Chojnowski_quality]]
| [[2023Fernandez_Local]]
| Quality of models automatically fitted with ARP/wARP
| Local defocus estimation
|-  
|-  


| Paper
| Paper
| [[2021Han_Vesper]]
| [[2025Elferich_CTFFind5]]
| VESPER: global and local cryo-EM map alignment using local density vectors
| Quality, tilt, and thickness of TEM samples with CTFFind5
|-  
|-  
|}
=== Segmentation ===
{|


| Paper
| Paper
| [[2021Lawson_Challenge]]
| [[2006Baker_segmentation]]
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
| Segmentation of molecular subunits
|-  
|-  


| Paper
| Paper
| [[2021Mori_Flexible]]
| [[2010Pintilie_segger]]
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
| Segmentation of molecular subunits
|-  
|-  


| Paper
| Conference
| [[2021Pfab_DeepTracer]]
| [[2017Nissenson_VolumeCut]]
| DeepTracer for fast de novo cryo-EM protein structure modeling
| Segmentation of an EM volume using an atomic model
|-  
|-  


| Paper
| Paper
| [[2021Saltzberg_IMP]]
| [[2019Beckers_FDR]]
| Using the Integrative Modeling Platform to model a cryoEM map
| Segmentation of the protein using False Discovery Rate
|-  
|-  


| Paper
| Paper
| [[2021Terwilliger_CryoID]]
| [[2020Beckers_FDR]]
| Identification of sequence in a CryoEM map from a set of candidates
| Segmentation of the protein using False Discovery Rate (GUI)
|-  
|-  


| Paper
| Paper
| [[2021Titarenko_LocalCorr]]
| [[2020Farkas_MemBlob]]
| Performance improvement of local correlation for docking
| Segmentation of membrane in membrane embedded proteins
|-  
|-  


| Conference
| Paper
| [[2021Vuillemot_NMA]]
| [[2020Terashi_MainMastSeg]]
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
| Segmentation of proteins into domains
|-  
|-  


| Paper
| Paper
| [[2022Antanasijevic_ab]]
| [[2022Ranno_Neural]]
| Sequence determination of antibodies bound to a map
| Neural representation of a map
|-  
|-  


| Paper
| Paper
| [[2022Behkamal_LPTD]]
| [[2021He_EMNUSS]]
| LPTD: Topology determination of CryoEM maps
| EMNUSS: Identification of secondary structure in CryoEM maps with deep learning
|-  
|-  


| Paper
| Paper
| [[2022Chojnowski_findMySeq]]
| [[2024Sazzed_CryoSSESeg]]
| Identify sequence in CryoEM map using Deep Learning
| CryoSSESeg: Identification of secondary structure in CryoEM maps with deep learning
|-  
|-  


| Paper
| Paper
| [[2022Hryc_Pathwalking]]
| [[2025Cao_EMInfo]]
| Atomic modelling with Pathwalking
| EMInfo: Segmentation of secondary structure and nucleic acids in CryoEM maps
|-  
|-  
|}
=== Fitting and docking ===
{|


| Paper
| Paper
| [[2022He_EMBuild]]
| [[1999Volkmann_Fitting]]
| Atomic modelling for complexes with EMbuild
| Fitting in real space
|-  
|-  


| Paper
| Paper
| [[2022Krieger_Prody2]]
| [[2001Baker_Review]]
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
| Review of protein structure prediction
|-  
|-  


| Paper
| Paper
| [[2022Neijenhuis_Haddock]]
| [[2001Jones_Review]]
| Protein-protein interface refinement in complex maps with Haddock2.4
| Review of protein structure prediction
|-  
|-  


| Paper
| Paper
| [[2022Urzhumtsev_Direct]]
| [[2003Kovacs_FRM3D]]
| Calculation of the EM map from an atomic model
| Fast Rotational Alignment of two EM maps
|-  
|-  


| Paper
| Paper
| [[2022Urzhumtsev_XrayEM]]
| [[2004Tama_NMA1]]
| Effect of the local resolution on the atomic modeling
| Flexible fitting with Normal Modes (I)
|-  
|-  


| Paper
| Paper
| [[2022Vuillemot_NMMD]]
| [[2004Tama_NMA2]]
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
| Flexible fitting with Normal Modes (II)
|-  
|-  


| Paper
| Paper
| [[2022Zhang_CRITASSER]]
| [[2005Velazquez_Superfamilies]]
| Atomic models of assemble protein structures with deep learning
| Recognition of the superfamily folding in medium-high resolution volumes
|-  
|-  


| Paper
| Paper
| [[2023Blau_FittingML]]
| [[2007DeVries_Haddock]]
| Maximum-likelihood fitting of atomic models in EM maps
| Docking with Haddock 2.0
|-  
|-  


| Paper
| Paper
| [[2023Chang_CryoFold]]
| [[2007Kleywegt_QualityControl]]
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
| Quality control and validation of fitting
|-  
|-  


| Paper
| Paper
| [[2023Millan_LL]]
| [[2008Orzechowski_Flexible]]
| Likelihood-based docking of models into cryo-EM maps
| Flexible fitting with biased molecular dynamics
|-  
|-  


| Paper
| Paper
| [[2023Park_CSA]]
| [[2008Rusu_Interpolation]]
| Atomic model fitting using conformational space annealing
| Biomolecular pleiomorphism probed by spatial interpolation of coarse models
|-  
|-  


| Paper
| Paper
| [[2023Read_LL]]
| [[2012Biswas_Secondary]]
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
| Secondary structure determination in EM volumes
|-  
|-  


| Paper
| Paper
| [[2023Reggiano_MEDIC]]
| [[2012Velazquez_Constraints]]
| Evaluation of atomic models using MEDIC
| Multicomponent fitting by using constraints from other information sources
|-  
|-  


| Paper
| Paper
| [[2023Richardson_Overfitting]]
| [[2013Chapman MS_Atomicmodeling]]
| Evaluation of overfitting errors in model building
| Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters
|-  
|-  


| Paper
| Paper
| [[2023Sweeney_ChemEM]]
| [[2013Esquivel_Modelling]]
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
| Review on modelling (secondary structure, fitting, ...)
|-  
|-  


| Paper
| Paper
| [[2023Terashi_DAQrefine]]
| [[2013Lopez_Imodfit]]
| Atomic model refinement using AlphaFold2 and DAQ
| Fitting based on vibrational analysis
|-  
|-  


| Paper
| Paper
| [[2023Terashi_DeepMainMast]]
| [[2013Nogales_3DEMLoupe]]
| DeepMainMast: de novo modelling of CryoEM maps
| Normal Mode Analysis of reconstructed volumes
|-  
|-  


| Paper
| Paper
| [[2023Terwilliger_AlphaFold]]
| [[2014AlNasr_Secondary]]
| Comparison of AlphaFold predictions with experimental maps and models
| Identification of secondary structure elements in EM volumes
|-  
|-  


| Paper
| Paper
| [[2023Wang_CryoREAD]]
| [[2014Politis_MassSpect]]
| CryoREAD: de novo modelling of nucleic acids
| Integration of mass spectroscopy information
|-  
|-  


| Paper
| Paper
| [[2024Beton_Ensemble]]
| [[2014Rey_MassSpect]]
| Ensemble fitting
| Integration of mass spectroscopy information
|-  
|-  


| Paper
| Paper
| [[2024He_SHOT]]
| [[2014Villa_Review]]
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
| Review of atomic fitting into EM volumes
|-  
|-  


| Paper
| Paper
| [[2024Li_EMRNA]]
| [[2015Barad_EMRinger]]
| EMRNA: de novo modeling of RNA structures
| Validation of hybrid models
|-  
|-  


|}
| Paper
 
| [[2015Bettadapura_PF2Fit]]
=== Books and reviews ===
| Fast rigid fitting of PDBs into EM maps
 
{|
 
| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-  
|-  


| Book
| Paper
| [[1988Kak_Tomography]]
| [[2015Carrillo_CapsidMaps]]
| General book on tomography
| Analysis of virus capsids using Google Maps
|-  
|-  


| Paper
| Paper
| [[2000Tao_Review]]
| [[2015Hanson_Continuum]]
| Review of single particles
| Modelling assemblies with continuum mechanics
|-  
|-  


| Paper
| Paper
| [[2000VanHeel_Review]]
| [[2015Lopez_Review]]
| Review of single particles
| Review of structural modelling from EM data
|-  
|-  


| Paper
| Paper
| [[2002Frank_Review]]
| [[2015Schroeder_Hybrid]]
| Review of single particles
| Review on model building
|-  
|-  


| Paper
| Paper
| [[2002Schmid_Review]]
| [[2015Tamo_Dynamics]]
| Review of single particles
| Dynamics in integrative modeling
|-  
|-  


| Paper
| Paper
| [[2004Henderson_Review]]
| [[2015Sorzano_AtomsToVoxels]]
| Review of electron microscopy
| Accurate conversion of an atomic model into a voxel density volume
|-  
|-  


| Paper
| Paper
| [[2004Subramaniam_Review]]
| [[2016Joseph_Evolution]]
| Review of single particles
| Evolutionary constraints for the fitting of atomic models into density maps
|-  
|-  


| Paper
| Paper
| [[2005Steven_Review]]
| [[2016Joseph_Refinement]]
| Review of electron microscopy
| Refinement of atomic models in high-resolution EM reconstructions using Flex-EM
|-  
|-  


| Paper
| Paper
| [[2006Fernandez_Review]]
| [[2016Murshudov_Refinement]]
| Review of electron microscopy
| Refinement of atomic models in high-resolution EM reconstructions
|-  
|-  


| Book
| Paper
| [[2006Frank_book]]
| [[2016Segura_3Diana]]
| Book covering all aspects of electron microscopy of single particles
| Validation of hybrid models
|-  
|-  


| Paper
| Paper
| [[2006Sorzano_Review]]
| [[2016Singharoy_MDFF]]
| Review of optimization problems in electron microscopy
| Construction of hybrid models driven by EM density and molecular dynamics
|-  
|-  


| Paper
| Paper
| [[2007Leschziner_Review]]
| [[2016Wang_Rosetta]]
| Review of 3D heterogeneity handling algorithms
| Construction of hybrid models driven by EM density using Rosetta
|-  
|-  


| Paper
| Paper
| [[2007Sorzano_Review]]
| [[2017Chen_CoarseGraining]]
| Review of the image processing steps
| Coarse graining of EM volumes
|-  
|-  


| Paper
| Paper
| [[2008Fanelli_ImageFormation]]
| [[2017Joseph_Metrics]]
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
| Metrics analysis for the comparison of structures
|-  
|-  


| Paper
| Paper
| [[2008Fernandez_HPCReview]]
| [[2017Hryc_WeightedAtoms]]
| High performance computing in electron cryomicroscopy
| Construction of hybrid models by locally weighting the different atoms
|-  
|-  


| Paper
| Paper
| [[2008Jonic_Review]]
| [[2017Matsumoto_Distribution]]
| Comparison between electron tomography and single particles
| Estimating the distribution of conformations of atomic models
|-  
|-  


| Paper
| Paper
| [[2008Mueller_Review]]
| [[2017Michel_ContactPrediction]]
| Review of Electron microscopy
| Structure prediction by contact prediction
|-  
|-  


| Paper
| Paper
| [[2008Taylor_Review]]
| [[2017Miyashita_EnsembleFitting]]
| Review of Electron microscopy
| Ensemble fitting using Molecular Dynamics
|-  
|-  


| Paper
| Paper
| [[2010DeRosier_Review]]
| [[2017Turk_ModelBuilding]]
| Personal account of how 3DEM developed in the early days
| Tutorial on model building and protein visualization
|-  
|-  


| Chapter
| Paper
| [[2012Sorzano_Review]]
| [[2017Wang_PartialCharges]]
| Review of single particle analysis using Xmipp
| Appearance of partial charges in EM maps
|-  
|-  


| Chapter
| Paper
| [[2012Devaux_Protocol]]
| [[2017Wlodawer]]
| Protocols for performing single particle analysis
| Comparison of X-ray and EM high resolution structures
|-  
|-  


| Paper
| Paper
| [[2014Bai_Review]]
| [[2018Cassidy_review]]
| Recent advances in cryo-EM
| Review of methods for hybrid modeling
|-  
|-  


| Paper
| Paper
| [[2015Carazo_Review]]
| [[2018Chen_SudeChains]]
| Review of the reconstruction process
| A comparison of side chains between X-ray and EM maps
|-  
|-  


| Paper
| Paper
| [[2015Cheng_Review]]
| [[2018Kawabata_Pseudoatoms]]
| A primer to Single Particle Cryo-EM
| Modelling the EM map with Gaussian pseudoatoms
|-  
|-  


| Paper
| Paper
| [[2015Cheng_Reviewb]]
| [[2018Kovacs_Medium]]
| Single Particle Cryo-EM at crystallographic resolution
| Modelling of medium resolution EM maps
|-  
|-  


| Paper
| Paper
| [[2015Elmlund_Review]]
| [[2018Neumann_validation]]
| Recent advances in cryo-EM
| Validation of fitting, resolution assessment and quality of fit
|-  
|-  


| Paper
| Paper
| [[2015Henderson_Review]]
| [[2018Terwilliger_map_to_model]]
| Recent advances in cryo-EM
| Phenix map_to_model, automatic modelling of EM volumes
|-  
|-  


| Paper
| Paper
| [[2015Nogales_Review]]
| [[2018Wang_MD]]
| Recent advances in cryo-EM
| Constructing atomic models using molecular dynamics
|-  
|-  


| Paper
| Paper
| [[2015Schroeder_Review]]
| [[2018Xia_MVPENM]]
| Review of advances in the electron microscope
| Multiscale Normal Mode Analysis
|-  
|-  


| Paper
| Paper
| [[2015VanDenBedem_Integrative]]
| [[2018Yu_Atomic]]
| Review of integrative structural biology
| Constructing atomic models using existing tools
|-  
|-  


| Paper
| Paper
| [[2015Wu_Review]]
| [[2019Bonomi_Multiscale]]
| Review of advances in cryo-EM
| Bayesian multi-scale modelling
|-  
|-  


| Paper
| Paper
| [[2016Carroni_CryoEM]]
| [[2019Kidmose_Namdinator]]
| Review of advances in Cryo-EM
| Namdinator: Flexible fitting with NAMD
|-  
|-  


| Paper
| Paper
| [[2016Egelman_CryoEM]]
| [[2019Kim_CryoFit]]
| Review of advances in Cryo-EM
| CryoFit: flexible fitting in Phoenix
|-  
|-  


| Paper
| Paper
| [[2016Eisenstein_CryoEM]]
| [[2019Klaholz_Review]]
| News feature on the Method of the Year
| Review of Phenix tools to modelling
|-  
|-  


| Paper
| Paper
| [[2016FernandezLeiro_Review]]
| [[2019Subramaniya_DeepSSE]]
| Review of EM
| Secondary structure prediction from maps using deep learning
|-  
|-  


| Paper
| Paper
| [[2016Glaeser_HowGood]]
| [[2019Zhang_CoarseGrained]]
| How good can cryo-EM become?
| Coarse-graining of EM maps
|-  
|-  


| Paper
| Paper
| [[2016Jonic_PseudoAtoms]]
| [[2020Costa_MDeNM]]
| Review of the applications of the use of pseudoatoms in EM
| Flexible fitting with molecular dynamics and normal modes
|-  
|-  


| Chapter
| Paper
| [[2016Mio_Review]]
| [[2020Cragnolini_Tempy2]]
| Overview of the process to obtain EM reconstructions
| TEMpy2 library for density-fitting and validation
|-  
|-  


| Paper
| Paper
| [[2016Jonic_Review]]
| [[2020Dodd_ModelBuilding]]
| A review of computational ways to handle heterogeneity
| Model building possibilities, with special emphasis on flexible fitting
|-  
|-  


| Paper
| Paper
| [[2016Nogales_Review]]
| [[2020Ho_CryoID]]
| Review of advances in cryo-EM
| Identification of proteins in structural proteomics from cryoEM volumes
|-  
|-  


| Paper
| Paper
| [[2016Subramaniam_Review]]
| [[2020Hoh_Buccaneer]]
| Why cryo-EM is now suitable for crystallographic journals
| Structure modelling with Buccaneer
|-  
|-  


| Paper
| Paper
| [[2016Vinothkumar_Review]]
| [[2020Joseph_comparison]]
| Historical review and current limitations
| Comparison of map and model, or two maps
|-  
|-  


| Report
| Paper
| [[2017Brezinski_Nobel]]
| [[2020Kim_Review]]
| Scientific background on the Nobel Prize in Chemistry 2017
| Review of the options for atomic modelling
|-  
|-  


| Paper
| Paper
| [[2017Cheng_review]]
| [[2020Leelananda_Constraints]]
| Why CryoEM became so hot
| NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD structure refinement
|-  
|-  


| Paper
| Paper
| [[2017Danev_Review]]
| [[2020Liebschner_Ceres]]
| Review of the use of phase plates in EM
| CERES: Web server of refined atomic maps of CryoEM deposited maps by Phenix
|-  
|-  


| Paper
| Paper
| [[2017Elmlund_Review]]
| [[2020Oroguchi]]
| Review of the main current difficulties of EM
| Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data
|-  
|-  


| Paper
| Paper
| [[2017Frank_Review]]
| [[2020Vant_Flexible]]
| Historical review of EM
| Flexible fitting with molecular dynamics and neural network potentials
|-  
|-  


| Paper
| Paper
| [[2017Frank_TimeResolved]]
| [[2021Behkamal_Secondary]]
| Review of time-resolved of EM
| Secondary structure from medium resolution maps
|-  
|-  


| Paper
| Paper
| [[2017Jonic_Review]]
| [[2021Chojnowski_quality]]
| Review of computational methods to analyze conformational variability
| Quality of models automatically fitted with ARP/wARP
|-  
|-  


| Paper
| Paper
| [[2017Merino_DrugEM]]
| [[2021Han_Vesper]]
| Applications of EM for drug design
| VESPER: global and local cryo-EM map alignment using local density vectors
|-  
|-  


| Paper
| Paper
| [[2017Rawson_Limitations]]
| [[2021Lawson_Challenge]]
| Limitations of EM for drug design
| Validation recommendations based on outcomes of the 2019 EMDataResource challenge
|-  
|-  


| Paper
| Paper
| [[2017Sorzano_FourierProperties]]
| [[2021Mori_Flexible]]
| Review of statistical properties of resolution measures defined in Fourier space
| Efficient Flexible Fitting Refinement with Automatic Error Fixing
|-  
|-  


| Paper
| Paper
| [[2017Sorzano_SurveyIterative]]
| [[2021Pfab_DeepTracer]]
| Survey of iterative reconstruction methods for EM
| DeepTracer for fast de novo cryo-EM protein structure modeling
|-  
|-  


| Paper
| Paper
| [[2018Bruggeman_Crowdsourcing]]
| [[2021Saltzberg_IMP]]
| Exploring crowdsourcing for EM image processing
| Using the Integrative Modeling Platform to model a cryoEM map
|-  
|-  


| Paper
| Paper
| [[2018Cheng_Review]]
| [[2021Terwilliger_CryoID]]
| Review of EM and future ahead
| Identification of sequence in a CryoEM map from a set of candidates
|-  
|-  


| Paper
| Paper
| [[2018Cossio_ML]]
| [[2021Titarenko_LocalCorr]]
| Review of Maximum Likelihood methods
| Performance improvement of local correlation for docking
|-  
|-  


| Paper
| Conference
| [[2018Grimes_Crystallography]]
| [[2021Vuillemot_NMA]]
| Review of X-ray crystallography and its relationship to EM
| Flexible fitting using a combined Bayesian and Normal Mode approach with Hamiltonian Monte Carlo sampling
|-  
|-  


| Paper
| Paper
| [[2018Murata_Review]]
| [[2022Antanasijevic_ab]]
| Review of EM for structure dynamics
| Sequence determination of antibodies bound to a map
|-  
|-  


| Paper
| Paper
| [[2018Quentin_Biomedical]]
| [[2022Behkamal_LPTD]]
| Review of EM as a tool for biomedical research
| LPTD: Topology determination of CryoEM maps
|-  
|-  


| Paper
| Paper
| [[2018Scapin_DrugDiscovery]]
| [[2022Bouvier_coevolution]]
| Review of EM as a tool for drug discovery
| Atomic modelling exploiting residue coevolution
|-  
|-  


| Paper
| Paper
| [[2018Vilas_ImageProcessing]]
| [[2022Chojnowski_findMySeq]]
| Review of the recent developments in image processing for single particle analysis
| Identify sequence in CryoEM map using Deep Learning
|-  
|-  


| Paper
| Paper
| [[2018vonLoeffelholz_VPP]]
| [[2022Hryc_Pathwalking]]
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
| Atomic modelling with Pathwalking
|-  
|-  


| Paper
| Paper
| [[2018Eisenstein_DrugDesigners]]
| [[2022He_EMBuild]]
| Drug designers embrace cryo-EM
| Atomic modelling for complexes with EMbuild
|-  
|-  


| Paper
| Paper
| [[2019Benjin_Review]]
| [[2022Krieger_Prody2]]
| Review of SPA
| Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0
|-  
|-  


| Paper
| Paper
| [[2019Danev_Review]]
| [[2022Neijenhuis_Haddock]]
| Review of future directions
| Protein-protein interface refinement in complex maps with Haddock2.4
|-  
|-  


| Paper
| Paper
| [[2019Lyumkis_Review]]
| [[2022Terwilliger_AlphaFold]]
| Challenges and reviews
| Iterative modelling with AlphaFold and experimental maps
|-  
|-  


| Paper
| Paper
| [[2019Sorzano_Review]]
| [[2022Urzhumtsev_Direct]]
| Review of continuous heterogeneity biophysics
| Calculation of the EM map from an atomic model
|-  
|-  


| Paper
| Paper
| [[2019Urzhumtseva_Review]]
| [[2022Urzhumtsev_XrayEM]]
| Review of rotation conventions
| Effect of the local resolution on the atomic modeling
|-  
|-  


| Paper
| Paper
| [[2020Abriata_Review]]
| [[2022Vuillemot_NMMD]]
| Considerations of structure prediction and CryoEM
| NMMD: Flexible fitting with simultaneous Normal Mode and Molecular Dynamics displacements
|-  
|-  


| Paper
| Paper
| [[2020Akbar_Review]]
| [[2022Zhang_CRITASSER]]
| Review of membrane protein reconstructions
| Atomic models of assemble protein structures with deep learning
|-  
|-  


| Paper
| Paper
| [[2020Bendory_Review]]
| [[2023Blau_FittingML]]
| Review of image processing problems
| Maximum-likelihood fitting of atomic models in EM maps
|-  
|-  


| Paper
| Paper
| [[2020Dubach_Review]]
| [[2023Chang_CryoFold]]
| Review of resolution in X-ray crystallography and CryoEM
| Flexible fitting into cryo-EM maps with CryoFold (a MELD plugin)
|-  
|-  


| TechReport
| Paper
| [[2020Lai_Statistics]]
| [[2023Dai_CryoFEM]]
| Review of statistical properties of image alignment
| CryoFEM: Deep learning+AlphaFold 2 for the interpretation of maps
|-  
|-  


| Paper
| Paper
| [[2020Hu_Quaternions]]
| [[2023Millan_LL]]
| Review of the use of quaternions to describe rotations
| Likelihood-based docking of models into cryo-EM maps
|-  
|-  


| Paper
| Paper
| [[2020McCafferty_Review]]
| [[2023Park_CSA]]
| Review of SPA and Mass Spectroscopy
| Atomic model fitting using conformational space annealing
|-  
|-  


| Paper
| Paper
| [[2020Seffernick_Hybrid]]
| [[2023Read_LL]]
| Review of hybrid (computational and experimental) methods to get protein structure
| Likelihood-based signal and noise analysis for docking of models into cryo-EM maps
|-  
|-  


| Paper
| Paper
| [[2020Nakane_Atomic]]
| [[2023Reggiano_MEDIC]]
| Single-particle cryo-EM at atomic resolution
| Evaluation of atomic models using MEDIC
|-  
|-  


| Paper
| Paper
| [[2020Vilas_Review]]
| [[2023Richardson_Overfitting]]
| Review of local resolution
| Evaluation of overfitting errors in model building
|-  
|-  


| Paper
| Paper
| [[2020Wu_Review]]
| [[2023Sweeney_ChemEM]]
| Review of current limitations, with special emphasis on protein size
| ChemEM: Flexible Docking of Small Molecules in Cryo-EM Structures
|-  
|-  


| Paper
| Paper
| [[2020Singer_Sigworth_Review]]
| [[2023Terashi_DAQrefine]]
| Review of single particle analysis
| Atomic model refinement using AlphaFold2 and DAQ
|-
|-  


| Paper
| Paper
| [[2021Bai_Review]]
| [[2023Terashi_DeepMainMast]]
| Review of breakthroughs leading to atomic resolution
| DeepMainMast: de novo modelling of CryoEM maps
|-
|-  


| Paper
| Paper
| [[2021DImprima_Review]]
| [[2023Terwilliger_AlphaFold]]
| Review of sample preparation for single particle analysis
| Comparison of AlphaFold predictions with experimental maps and models
|-
|-  


| Paper
| Paper
| [[2021Lander_Review]]
| [[2023Wang_CryoREAD]]
| Review of focused analysis in SPA
| CryoREAD: de novo modelling of nucleic acids
|-
|-  


| Paper
| Paper
| [[2021Raimondi_Review]]
| [[2024Beton_Ensemble]]
| General review of SPA
| Ensemble fitting
|-
|-  


| Paper
| Paper
| [[2022Beton_Fitting]]
| [[2024Chen_EModelX]]
| Review of fitting in SPA
| Atomic modelling de novo from cryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Burley_PDB]]
| [[2024Dahmani_MDFF]]
| Review of cryoEM derived structures at PDB
| Accelerated MDFF flexible fitting
|-
|-  


| Paper
| Paper
| [[2022Caldraft_Tilt]]
| [[2024Giri_CryoStruct]]
| Review of applications of tilt pairs in SPA
| CryoStruct: de novo modeling of cryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Donnat_GAN]]
| [[2024Gucwa_CMM]]
| Review of Generative modelling with neural networks
| CheckMyMetal: Metal analysis in CryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Guaita_Review]]
| [[2024Jamali_Modelangelo]]
| Recent advances and current trends in cryo-electron microscopy
| ModelAngelo: Automated model building of cryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Jones_Comment]]
| [[2024He_SHOT]]
| Comment on the impact of AlphaFold and next challenges ahead
| Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
|-
|-  


| Paper
| Paper
| [[2022Namba_Review]]
| [[2024Hoff_EMMIVox]]
| Review of the current state of SPA
| EMMIVox: Model fitting using ensembles and molecular dynamics
|-
|-  


| Paper
| Paper
| [[2022Ourmazd_Comment]]
| [[2024Li_EMRNA]]
| Comment on the impact of AlphaFold and next challenges ahead
| EMRNA: de novo modeling of RNA structures
|-
|-  


| Paper
| Paper
| [[2022Palmer_Local]]
| [[2024Li_EM2NA]]
| Review of local methods in CryoEM
| EM2NA: Detection and de novo modelling of nucleic acids in cryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Sorzano_1000]]
| [[2024Read_Interactive]]
| CryoEM is the field of 1000+ methods
| Interactive local docking
|-
|-  


| Paper
| Paper
| [[2022Subramaniam_Comment]]
| [[2024Wang_DiffModeller]]
| Comment on the impact of AlphaFold and next challenges ahead
| CryoEM map modelling integrating AlphaFold2 and diffusion networks
|-
|-  


| Paper
| Paper
| [[2022Treder_DL]]
| [[2024Wankowicz_qFit]]
| Review of Deep Learning applications in CryoEM
| Multiconformer modeling of cryoEM maps
|-
|-  


| Paper
| Paper
| [[2022Vant_MD]]
| [[2024Wlodarski_cryoEnsemble]]
| Review of Molecular Dynamics analysis of CryoEM maps
| CryoEnsemble: cryoEM map interpretation with molecular dynamics simulated ensembles
|-
|-  


| Paper
| Paper
| [[2023Amann_TimeResolved]]
| [[2025Carr_Map2Seq]]
| Review of time-resolved cryoEM
| Map-to-sequence workflow
|-
|-  


| Paper
| Paper
| [[2023Bai_Challenges]]
| [[2025Chen_GMMs]]
| Challenges and opportunities in structure determination
| Model building in heterogeneous maps
|-
|-  


| Paper
| Paper
| [[2023Liu_AWI]]
| [[2025Haloi_Ligand]]
| Review of the Air-Water Interface
| Ligand detection in CryoEM maps using structure prediction and flexible fitting
|-
|-  


| Paper
| Paper
| [[2023Lucas_Structureome]]
| [[2025Karolczak_Ligand]]
| Review of the localization of proteins and complexes in their cellular context
| Ligand detection in CryoEM maps using deep learning
|-
|-  


| Paper
| Paper
| [[2023Miyashita_MD]]
| [[2025Luo_DiffFit]]
| Review of the use of molecular dynamics in atomic modelling
| DiffFit: Flexible fitting of map and atomic model
|-
|-  


| Paper
| Paper
| [[2023Si_DeNovo]]
| [[2025Mallet_crAI]]
| Review of the de-novo atomic modelling
| crAI: detection of antibodies in cryoEM maps
|-
|-  


| Paper
| Paper
| [[2023Tang_Conformational]]
| [[2025Matsuoka_ForceConstant]]
| Review of conformational heterogeneity and probability distributions
| Empirical determination of the force constant for flexible fitting
|-
|-  


| Paper
| Paper
| [[2023Toader_Heterogeneity]]
| [[2025Muenks_EmeraldID]]
| Review of continuous heterogeneity
| Emerald ID: Identification of small ligands in cryoEM maps
|-
|-  


| Paper
| Paper
| [[2024Riggi_Animation]]
| [[2025Riahi_EMPOT]]
| Review of 3D animation as a tool for integrative modeling
| EMPOT: aligning partially overlapping maps using Unbalanced Gromov-Wasserstein Divergence
|-
|-  
 
|}
 
=== Software ===
 
{|


| Paper
| Paper
| [[1996Frank_Spider]]
| [[2025Shub_Mic]]
| Spider
| Mic: a deep learning algorithm to assign ions and waters in SPA maps
|-  
|-  


| Paper
| Paper
| [[1996VanHeel_Imagic]]
| [[2025Su_CryoAtom]]
| Imagic
| CryoAtom: Model building using deep learning
|-  
|-  


| Paper
| Paper
| [[1999Lutdke_Eman]]
| [[2025Wang_E3CryoFold]]
| Eman
| E3CryoFold: model building in cryoEM maps
|-  
|-  


| Paper
| Paper
| [[2004Sorzano_Xmipp]]
| [[2025Zhang_Emol]]
| Xmipp
| Emol: modeling protein-nucleic acid complex structures from cryo-EM maps
|-  
|-  


| Paper
| Paper
| [[2007Baldwin_AngularTransformations]]
| [[2025Zhang_Benchmark]]
| The Transform Class in SPARX and EMAN2
| Benchmarking multiple algorithms to compute an atomic model from a cryoEM map
|-  
|-  


| Paper
| Paper
| [[2007Heymann_Bsoft]]
| [[2025Zheng_Disorder]]
| Bsoft
| Exploration of disordered regions in CryoEM maps
|-  
|-  


| Paper
|}
| [[2007Grigorieff_Frealign]]
 
| Frealign
=== Books and reviews ===
 
{|
 
| Book
| [[1980Herman_Tomography]]
| General book on tomography
|-  
|-  


| Paper
| Book
| [[2008Scheres_XmippProtocols]]
| [[1988Kak_Tomography]]
| Xmipp Protocols
| General book on tomography
|-  
|-  


| Paper
| Paper
| [[2008Shaikh_SpiderProtocols]]
| [[2000Tao_Review]]
| Spider Protocols
| Review of single particles
|-  
|-  


| Paper
| Paper
| [[2012Wriggers_SitusConventions]]
| [[2000VanHeel_Review]]
| Conventions and workflows in Situs
| Review of single particles
|-  
|-  


| Paper
| Paper
| [[2013DeLaRosa_Xmipp30]]
| [[2002Frank_Review]]
| Xmipp 3.0
| Review of single particles
|-  
|-  


| Paper
| Paper
| [[2015Cianfrocco_Cloud]]
| [[2002Schmid_Review]]
| Software execution in the cloud
| Review of single particles
|-  
|-  


| Paper
| Paper
| [[2015Cheng_MRC2014]]
| [[2004Henderson_Review]]
| Extensions to MRC file format
| Review of electron microscopy
|-  
|-  


| Paper
| Paper
| [[2013DeLaRosa_Scipion]]
| [[2004Subramaniam_Review]]
| Scipion
| Review of single particles
|-  
|-  


| Paper
| Paper
| [[2016Scheres_Relion]]
| [[2005Steven_Review]]
| Tutorial on the use of Relion
| Review of electron microscopy
|-  
|-  


| Paper
| Paper
| [[2016Grigorieff_Frealign]]
| [[2006Fernandez_Review]]
| Tutorial on the use of Frealign
| Review of electron microscopy
|-  
|-  


| Paper
| Book
| [[2017Moriya_Sphire]]
| [[2006Frank_book]]
| Tutorial on the use of Sphire
| Book covering all aspects of electron microscopy of single particles
|-  
|-  


| Paper
| Paper
| [[2018Bell_EMAN2]]
| [[2006Sorzano_Review]]
| New tools in EMAN2
| Review of optimization problems in electron microscopy
|-  
|-  


| Paper
| Paper
| [[2018Cianfrocco_cloud]]
| [[2007Leschziner_Review]]
| CryoEM Cloud Tools
| Review of 3D heterogeneity handling algorithms
|-  
|-  


| Paper
| Paper
| [[2018Grant_cisTEM]]
| [[2007Sorzano_Review]]
| cisTEM
| Review of the image processing steps
|-  
|-  


| Paper
| Paper
| [[2018McLeod_MRCZ]]
| [[2008Fanelli_ImageFormation]]
| MRC Compression format
| Review on the image formation model from the electron waves and open inverse-problems in Electron Tomography
|-  
|-  


| Paper
| Paper
| [[2018Zivanov_Relion3]]
| [[2008Fernandez_HPCReview]]
| Relion 3
| High performance computing in electron cryomicroscopy
|-  
|-  


| Paper
| Paper
| [[2020Caesar_Simple3]]
| [[2008Jonic_Review]]
| Simple 3
| Comparison between electron tomography and single particles
|-  
|-  


| Paper
| Paper
| [[2021Baldwin_SCF]]
| [[2008Mueller_Review]]
| Visualizer of the Sampling Compensation Factor
| Review of Electron microscopy
|-  
|-  


| Paper
| Paper
| [[2021Jimenez_Scipion]]
| [[2008Taylor_Review]]
| Scipion workflow example for image processing
| Review of Electron microscopy
|-  
|-  


| Paper
| Paper
| [[2021Kimanius_Relion4]]
| [[2010DeRosier_Review]]
| Changes in Relion 4.0
| Personal account of how 3DEM developed in the early days
|-  
|-  


| Paper
| Chapter
| [[2021Maji_BlackBox]]
| [[2012Sorzano_Review]]
| Exploration of image processing concepts
| Review of single particle analysis using Xmipp
|-  
|-  


| Paper
| Chapter
| [[2021Sharov_Relion]]
| [[2012Devaux_Protocol]]
| Use of Relion within Scipion
| Protocols for performing single particle analysis
|-  
|-  


| Paper
| Paper
| [[2021Sorzano_Scipion]]
| [[2014Bai_Review]]
| Use of Scipion as a way to compare the results of multiple methods
| Recent advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[2021Strelak_Xmipp]]
| [[2015Carazo_Review]]
| Advances in Xmipp
| Review of the reconstruction process
|-  
|-  


| Paper
| Paper
| [[2022DiIorio_Multiple]]
| [[2015Cheng_Review]]
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
| A primer to Single Particle Cryo-EM
|-  
|-  


| Paper
| Paper
| [[2022Fluty_Precision]]
| [[2015Cheng_Reviewb]]
| Precision requirements and data compression
| Single Particle Cryo-EM at crystallographic resolution
|-  
|-  


| Paper
| Paper
| [[2022Harastani_ContinuousFlex]]
| [[2015Elmlund_Review]]
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
| Recent advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[2023Cheng_AutoEMage]]
| [[2015Henderson_Review]]
| AutoEMage: a system for processing in streaming (SPA)
| Recent advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[2023Conesa_Scipion3]]
| [[2015Nogales_Review]]
| Scipion3: A workflow engine for cryoEM
| Recent advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[2023Krieger_ScipionPrody]]
| [[2015Schroeder_Review]]
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
| Review of advances in the electron microscope
|-  
|-  


| Paper
| Paper
| [[2023Matinyan_TRPX]]
| [[2015VanDenBedem_Integrative]]
| TRPX compression format
| Review of integrative structural biology
|-  
|-  


| Paper
| Paper
| [[2023Short_MRC2020]]
| [[2015Wu_Review]]
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
| Review of advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[2024Gonzalez_Dashboard]]
| [[2016Carroni_CryoEM]]
| A web-based dashboard for Relion
| Review of advances in Cryo-EM
|-  
|-  


| Paper
| Paper
| [[2024Herre_Capsules]]
| [[2016Egelman_CryoEM]]
| SBGrid Capsules to execute programs in controlled environments
| Review of advances in Cryo-EM
|-  
|-  


| Paper
| Paper
| [[2024Vuillemot_MDSPACE]]
| [[2016Eisenstein_CryoEM]]
| MDSpace and MDTomo to analyze continuous heterogeneity
| News feature on the Method of the Year
|-  
|-  


|}
| Paper
 
| [[2016FernandezLeiro_Review]]
== Electron tomography ==
| Review of EM
 
|-
=== Image preprocessing ===
 
{|


| Paper
| Paper
| [[2015Yan_thickness]]
| [[2016Glaeser_HowGood]]
| Determination of thickness, tilt and electron mean free path
| How good can cryo-EM become?
|-  
|-  


| Paper
| Paper
| [[2018Wu_contrast]]
| [[2016Jonic_PseudoAtoms]]
| Contrast enhancement to improve alignability
| Review of the applications of the use of pseudoatoms in EM
|-  
|-  


|}
| Chapter
| [[2016Mio_Review]]
| Overview of the process to obtain EM reconstructions
|-


=== Image alignment ===
| Paper
 
| [[2016Jonic_Review]]
{|
| A review of computational ways to handle heterogeneity
|-


| Paper
| Paper
| [[1982Guckenberger_commonOrigin]]
| [[2016Nogales_Review]]
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
| Review of advances in cryo-EM
|-  
|-  


| Paper
| Paper
| [[1992Lawrence_leastSquares]]
| [[2016Subramaniam_Review]]
| Least squares solution of the alignment problem
| Why cryo-EM is now suitable for crystallographic journals
|-  
|-  


| Paper
| Paper
| [[1995Penczek_dual]]
| [[2016Vinothkumar_Review]]
| Dual tilt alignment
| Historical review and current limitations
|-  
|-  


| Paper
| Report
| [[1996Owen_alignmentQuality]]
| [[2017Brezinski_Nobel]]
| Automatic alignment without fiducial markers and evaluation of alignment quality
| Scientific background on the Nobel Prize in Chemistry 2017
|-  
|-  


| Paper
| Paper
| [[1998Grimm_normalization]]
| [[2017Cheng_review]]
| Discussion of several gray level normalization methods for electron tomography
| Why CryoEM became so hot
|-  
|-  


| Paper
| Paper
| [[2001Brandt_Automatic1]]
| [[2017Danev_Review]]
| Automatic alignment without fiducial markers
| Review of the use of phase plates in EM
|-  
|-  


| Paper
| Paper
| [[2001Brandt_Automatic2]]
| [[2017Elmlund_Review]]
| Automatic alignment with fiducial markers
| Review of the main current difficulties of EM
|-  
|-  


| Paper
| Paper
| [[2006Winkler_alignment]]
| [[2017Frank_Review]]
| Marker-free alignment and refinement
| Historical review of EM
|-  
|-  


| Paper
| Paper
| [[2006Castano_alignment]]
| [[2017Frank_TimeResolved]]
| Alignment with non-perpendicularity
| Review of time-resolved of EM
|-
|-  


| Paper
| Paper
| [[2007Castano_alignment]]
| [[2017Jonic_Review]]
| Fiducial-less alignment of cryo-sections
| Review of computational methods to analyze conformational variability
|-  
|-  


| Paper
| Paper
| [[2009Sorzano_alignment]]
| [[2017Merino_DrugEM]]
| Marker-free alignment and refinement
| Applications of EM for drug design
|-  
|-  


| Paper
| Paper
| [[2010Cantele_dualAlignment]]
| [[2017Rawson_Limitations]]
| Alignment of dual series
| Limitations of EM for drug design
|-  
|-  


| Paper
| Paper
| [[2014Tomonaga_Automatic]]
| [[2017Sorzano_FourierProperties]]
| Automatic alignment of tilt series using the projection themselves
| Review of statistical properties of resolution measures defined in Fourier space
|-  
|-  


| Paper
| Paper
| [[2014Han_Automatic]]
| [[2017Sorzano_SurveyIterative]]
| Automatic alignment of tilt series using SIFT features
| Survey of iterative reconstruction methods for EM
|-  
|-  


| Paper
| Paper
| [[2015Han_Automatic]]
| [[2018Bruggeman_Crowdsourcing]]
| Automatic alignment of tilt series using fiducials
| Exploring crowdsourcing for EM image processing
|-  
|-  


| Paper
| Paper
| [[2017Mastronarde_Automatic]]
| [[2018Cheng_Review]]
| Automatic alignment and reconstruction of tilt series in IMOD
| Review of EM and future ahead
|-  
|-  


| Paper
| Paper
| [[2018Fernadez_Beam]]
| [[2018Cossio_ML]]
| Image alignment considering beam induced motion
| Review of Maximum Likelihood methods
|-  
|-  


| Paper
| Paper
| [[2018Han_Fast]]
| [[2018Grimes_Crystallography]]
| Automatic alignment using fiducial markers
| Review of X-ray crystallography and its relationship to EM
|-  
|-  


| Paper
| Paper
| [[2019Fernandez_residual]]
| [[2018Murata_Review]]
| Alignment of tilt series using residual interpolation
| Review of EM for structure dynamics
|-  
|-  


| Paper
| Paper
| [[2019Han_Dual]]
| [[2018Quentin_Biomedical]]
| Automatic alignment using fiducial markers in dual tilt series
| Review of EM as a tool for biomedical research
|-  
|-  


| Paper
| Paper
| [[2020Sorzano_automatic]]
| [[2018Scapin_DrugDiscovery]]
| Automatic alignment considering several geometrical distortions
| Review of EM as a tool for drug discovery
|-  
|-  


| Paper
| Paper
| [[2021Han_LocalConstraints]]
| [[2018Vilas_ImageProcessing]]
| Automatic alignment considering local constraints
| Review of the recent developments in image processing for single particle analysis
|-  
|-  


| Paper
| Paper
| [[2022Zheng_Aretomo]]
| [[2018vonLoeffelholz_VPP]]
| Automatic alignment based on projection matching
| Comparison of Volta Phase Plate reconstructions close to focus and with defocus
|-  
|-  


| Paper
| Paper
| [[2024deIsidro_deep]]
| [[2018Eisenstein_DrugDesigners]]
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
| Drug designers embrace cryo-EM
|-  
|-  


| Paper
| Paper
| [[2024Hou_Marker]]
| [[2019Benjin_Review]]
| Marker detection using wavelets
| Review of SPA
|-  
|-  


|}
| Paper
 
| [[2019Danev_Review]]
=== CTF estimation and restoration ===
| Review of future directions
 
|-
{|


| Paper
| Paper
| [[2003Winkler_CTF]]
| [[2019Lyumkis_Review]]
| Focus gradient correction in electron tomography
| Challenges and reviews
|-  
|-  


| Paper
| Paper
| [[2006Fernandez_CTF]]
| [[2019Sorzano_Review]]
| CTF determination and correction in electron tomography
| Review of continuous heterogeneity biophysics
|-  
|-  


| Paper
| Paper
| [[2009Zanetti_CTF]]
| [[2019Urzhumtseva_Review]]
| CTF determination and correction in electron tomography
| Review of rotation conventions
|-  
|-  


| Paper
| Paper
| [[2009Xiong_CTF]]
| [[2020Abriata_Review]]
| CTF determination and correction for low dose tomographic tilt series
| Considerations of structure prediction and CryoEM
|-  
|-  


| Paper
| Paper
| [[2012Eibauer_CTF]]
| [[2020Akbar_Review]]
| CTF determination and correction
| Review of membrane protein reconstructions
|-  
|-  


| Paper
| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| [[2020Bendory_Review]]
| Subtomogram averaging with CTF correction using a Bayesian prior
| Review of image processing problems
|-  
|-  


| Paper
| Paper
| [[2017Turonova_3DCTF]]
| [[2020Dubach_Review]]
| 3D CTF Correction
| Review of resolution in X-ray crystallography and CryoEM
|-  
|-  


| Paper
| TechReport
| [[2017Kunz_3DCTF]]
| [[2020Lai_Statistics]]
| 3D CTF Correction
| Review of statistical properties of image alignment
|-  
|-  


| Paper
| Paper
| [[2024Mastronarded_CTFPlotter]]
| [[2020Hu_Quaternions]]
| CTF estimation with CTFPlotter
| Review of the use of quaternions to describe rotations
|-  
|-  
|}
=== 3D reconstruction ===
{|


| Paper
| Paper
| [[1972Gilbert_SIRT]]
| [[2020McCafferty_Review]]
| Simultaneous Iterative Reconstruction Technique (SIRT)
| Review of SPA and Mass Spectroscopy
|-  
|-  


| Paper
| Paper
| [[1973Herman_ART]]
| [[2020Seffernick_Hybrid]]
| Algebraic Reconstruction Technique (ART)
| Review of hybrid (computational and experimental) methods to get protein structure
|-  
|-  


| Paper
| Paper
| [[1984Andersen_SART]]
| [[2020Nakane_Atomic]]
| Simultaneous Algebraic Reconstruction Technique (SART)
| Single-particle cryo-EM at atomic resolution
|-  
|-  


| Paper
| Paper
| [[1992Radermacher_WBP]]
| [[2020Singer_Sigworth_Review]]
| Weighted Backprojection in electron tomography
| Review of single particle analysis
|-  
|-


| Paper
| Paper
| [[1997Marabini_reconstruction]]
| [[2020Vilas_Review]]
| Iterative reconstruction in electron tomography
| Review of local resolution
|-  
|-  


| Paper
| Paper
| [[2002Fernandez_reconstruction]]
| [[2020Wigge_Review]]
| Iterative reconstruction in electron tomography
| Review of drug discovery with CryoEM
|-  
|-  


| Paper
| Paper
| [[2007Radermacher_WBP]]
| [[2020Wu_Review]]
| Weighted Backprojection in electron tomography
| Review of current limitations, with special emphasis on protein size
|-  
|-  


| Paper
| Paper
| [[2008Fernandez_CARP]]
| [[2021Bai_Review]]
| Component Averaged Row Projections (CARP)
| Review of breakthroughs leading to atomic resolution
|-  
|-


| Paper
| Paper
| [[2010Xu_Long]]
| [[2021DImprima_Review]]
| Iterative reconstructions with long object correction and GPU implementation
| Review of sample preparation for single particle analysis
|-  
|-


| Paper
| Paper
| [[2012Herman General Superiorization]]
| [[2021Lander_Review]]
| Superiorization: an optimization heuristic for medical physics
| Review of focused analysis in SPA
|-  
|-


| Paper
| Paper
| [[2012Zhang_IPET_FETR]]
| [[2021Raimondi_Review]]
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
| General review of SPA
|-  
|-


| Paper
| Paper
| [[2013Goris_SIRT_TV_DART]]
| [[2022Beton_Fitting]]
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
| Review of fitting in SPA
|-  
|-


| Paper
| Paper
| [[2013Briegel A_Challenge]]
| [[2022Burley_PDB]]
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
| Review of cryoEM derived structures at PDB
|-  
|-


| Paper
| Paper
| [[2013Messaoudi_EnergyFiltered]]
| [[2022Caldraft_Tilt]]
| 3D Reconstruction of Energy-Filtered TEM
| Review of applications of tilt pairs in SPA
|-  
|-


| Paper
| Paper
| [[2014Paavolainen_Missing]]
| [[2022Donnat_GAN]]
| Compensation of the missing wedge
| Review of Generative modelling with neural networks
|-  
|-


| Paper
| Paper
| [[2015Venkatakrishnan_MBIR]]
| [[2022Guaita_Review]]
| 3D Reconstruction with priors
| Recent advances and current trends in cryo-electron microscopy
|-  
|-


| Paper
| Paper
| [[2016Deng_ICON]]
| [[2022Jones_Comment]]
| 3D Reconstruction with missing information restoration
| Comment on the impact of AlphaFold and next challenges ahead
|-  
|-


| Paper
| Paper
| [[2016Guay_Compressed]]
| [[2022Namba_Review]]
| 3D Reconstruction using compressed sensing
| Review of the current state of SPA
|-  
|-


| Paper
| Paper
| [[2016Turonova_Artifacts]]
| [[2022Ourmazd_Comment]]
| Artifacts observed during 3D reconstruction
| Comment on the impact of AlphaFold and next challenges ahead
|-  
|-


| Paper
| Paper
| [[2019Yan_MBIR]]
| [[2022Palmer_Local]]
| 3D Reconstruction with priors and demonstration of its use in biological samples
| Review of local methods in CryoEM
|-  
|-


| Paper
| Paper
| [[2020Sanchez_Hybrid]]
| [[2022Sorzano_1000]]
| 3D reconstruction with a special acquisition and alignment scheme
| CryoEM is the field of 1000+ methods
|-  
|-


| Paper
| Paper
| [[2020Song_Tygress]]
| [[2022Subramaniam_Comment]]
| 3D reconstruction with a special acquisition and alignment scheme
| Comment on the impact of AlphaFold and next challenges ahead
|-  
|-


| Paper
| Paper
| [[2021Fernandez_TomoAlign]]
| [[2022Treder_DL]]
| 3D reconstruction with sample motion and CTF correction
| Review of Deep Learning applications in CryoEM
|-
|-


| Paper
| Paper
| [[2021Geng_Nudim]]
| [[2022Vant_MD]]
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
| Review of Molecular Dynamics analysis of CryoEM maps
|-
|-


|}
| Paper
 
| [[2023Amann_TimeResolved]]
=== Noise reduction ===
| Review of time-resolved cryoEM
{|
|-


| Paper
| Paper
| [[2001Frangakis_NAD]]
| [[2023Bai_Challenges]]
| Noise reduction with Nonlinear Anisotropic Diffusion
| Challenges and opportunities in structure determination
|-  
|-


| Paper
| Paper
| [[2003Fernandez_AND]]
| [[2023Beton_Fitting]]
| Anisotropic nonlinear diffusion for electron tomography
| Review of fitting tools in cryoEM
|-  
|-


| Paper
| Paper
| [[2003Jiang_Bilateral]]
| [[2023DiIorio_AbInitio]]
| Bilateral denoising filter in electron microscopy
| Review of ab initio reconstruction algorithms based on deep learning
|-  
|-


| Paper
| Paper
| [[2005Fernandez_AND]]
| [[2023Liu_AWI]]
| Anisotropic nonlinear denoising in electron tomography
| Review of the Air-Water Interface
|-  
|-


| Paper
| Paper
| [[2007Heide_median]]
| [[2023Lucas_Structureome]]
| Iterative median filtering in electron tomography
| Review of the localization of proteins and complexes in their cellular context
|-
|-


| Paper
| Paper
| [[2007Fernandez_autAND]]
| [[2023Miyashita_MD]]
| Anisotropic nonlinear diffusion with automated parameter tuning
| Review of the use of molecular dynamics in atomic modelling
|-
|-


| Paper
| Paper
| [[2009Fernandez_Beltrami]]
| [[2023Si_DeNovo]]
| Nonlinear filtering based on Beltrami flow
| Review of the de-novo atomic modelling
|-  
|-
 
| Paper
| Paper
| [[2010Bilbao_MeanShift]]
| [[2023Tang_Conformational]]
| Mean Shift Filtering
| Review of conformational heterogeneity and probability distributions
|-  
|-


| Paper
| Paper
| [[2014Kovacik_wedgeArtefacts]]
| [[2023Toader_Heterogeneity]]
| Removal of wedge artefacts
| Review of continuous heterogeneity
|-  
|-


| Paper
| Paper
| [[2014Maiorca_beadArtefacts]]
| [[2024Bock_MD]]
| Removal of gold bead artefacts
| Review of the joint use of Molecular Dynamics and CryoEM
|-  
|-


| Paper
| Paper
| [[2018Trampert_Inpainting]]
| [[2024Bowlby_Flexible]]
| Removal of the missing wedge by inpainting
| Review of continuous flexibility
|-  
|-


| Paper
| Paper
| [[2018Moreno_TomoEED]]
| [[2024Cheng_Automated]]
| Fast Anisotropic Diffusion
| Review of automated acquisition
|-  
|-


| Paper
| Paper
| [[2018Wu_Enhancement]]
| [[2024Kimanius_Heterogeneity]]
| Enhancing the image contrast of electron tomography
| Review of heterogeneity analysis
|-  
|-


| Paper
| Paper
| [[2022Liu_Isonet]]
| [[2024Lander_Validation]]
| Isotropic reconstructions using deep learning
| Review of SPA validation
|-  
|-


|}
| Paper
| [[2024Riggi_Animation]]
| Review of 3D animation as a tool for integrative modeling
|-


=== Segmentation ===
| Paper
 
| [[2025Farheen_Modeling]]
{|
| Review of structure modeling
|-


| Paper
| Paper
| [[2002Frangakis_Eigenanalysis]]
| [[2025Patwardhan_Extending]]
| Segmentation using eigenvector analysis.
| Perspective on technological developments leading to a wider application of cryoEM
|-  
|-


| Paper
| Paper
| [[2002Volkmann_Watershed]]
| [[2025Wan_CryoETStandards]]
| Segmentation using watershed transform.
| Perspective on the need for CryoET standards
|-  
|-


| Paper
| Paper
| [[2003Bajaj_BoundarySegmentation]]
| [[2025Zhu_Quality]]
| Segmentation based on fast marching.
| Review of AI-based quality assessment of SPA maps
|-
|-
|}
=== Software ===
{|


| Paper
| Paper
| [[2005Cyrklaff_Thresholding]]
| [[1996Frank_Spider]]
| Segmentation using optimal thresholding.
| Spider
|-  
|-  


| Paper
| Paper
| [[2007Lebbink_TemplateMatching]]
| [[1996VanHeel_Imagic]]
| Segmentation using template matching.
| Imagic
|-  
|-  


| Paper
| Paper
| [[2007Sandberg_OrientationFields]]
| [[1999Lutdke_Eman]]
| Segmentation using orientation fields.
| Eman
|-  
|-  


| Paper
| Paper
| [[2007Sandberg_SegmentationReview]]
| [[2004Sorzano_Xmipp]]
| Review on segmentation in electron tomography.
| Xmipp
|-  
|-  


| Paper
| Paper
| [[2008Garduno_FuzzySegmentation]]
| [[2007Baldwin_AngularTransformations]]
| Segmentation using fuzzy set theory principles.
| The Transform Class in SPARX and EMAN2
|-  
|-  


| Paper
| Paper
| [[2009Lebbink_TemplateMatching2]]
| [[2007Heymann_Bsoft]]
| Segmentation using template matching.
| Bsoft
|-  
|-  


| Paper
| Paper
| [[2012RubbiyaAli_EdgeDetection]]
| [[2007Grigorieff_Frealign]]
| Parameter-Free Segmentation of Macromolecular Structures.
| Frealign
|-
 
| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-  
|-  


| Paper
| Paper
| [[2017Ali_RAZA]]
| [[2008Scheres_XmippProtocols]]
| Automated segmentation of tomograms
| Xmipp Protocols
|-  
|-  


| Paper
| Paper
| [[2017Chen_Annotation]]
| [[2008Shaikh_SpiderProtocols]]
| Automated annotation of tomograms
| Spider Protocols
|-  
|-  


| Paper
| Paper
| [[2017Tasel_ActiveContours]]
| [[2012Wriggers_SitusConventions]]
| Segmentation with active contours
| Conventions and workflows in Situs
|-  
|-  


| Paper
| Paper
| [[2017Xu_DeepLearning]]
| [[2013DeLaRosa_Xmipp30]]
| Finding proteins in tomograms using deep learning
| Xmipp 3.0
|-  
|-  


| Paper
| Paper
| [[2018Zeng_DeepLearning]]
| [[2015Cianfrocco_Cloud]]
| Mining features in Electron Tomography by deep learning
| Software execution in the cloud
|-  
|-  


| Paper
| Paper
| [[2020Salfer_PyCurv]]
| [[2015Cheng_MRC2014]]
| Curvature analysis of segmented tomograms
| Extensions to MRC file format
|-  
|-  


| Paper
| Paper
| [[2021Dimchev_filaments]]
| [[2013DeLaRosa_Scipion]]
| Segmentation of filaments in tomograms
| Scipion
|-  
|-  


| Paper
| Paper
| [[2022Frangakis_Curvature]]
| [[2016Scheres_Relion]]
| Use of mean curvature for segmentation and visualization of tomograms
| Tutorial on the use of Relion
|-  
|-  


| Paper
| Paper
| [[2022Lamm_MemBrain]]
| [[2016Grigorieff_Frealign]]
| Membrane segmentation using deep learning
| Tutorial on the use of Frealign
|-  
|-  


| Paper
| Paper
| [[2023Zeng_AITOM]]
| [[2017Moriya_Sphire]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
| Tutorial on the use of Sphire
|-  
|-  


| Paper
| Paper
| [[2024Gao_DomainFit]]
| [[2018Bell_EMAN2]]
| Protein identification in tomograms by mass spectroscopy, AlphaFold2 and domain fitting
| New tools in EMAN2
|-  
|-  
|}
=== Resolution ===
{|


| Paper
| Paper
| [[2005Cardone_Resolution]]
| [[2018Cianfrocco_cloud]]
| Resolution criterion for electron tomography
| CryoEM Cloud Tools
|-  
|-  


| Chapter
| Paper
| [[2007Penczek_Resolution]]
| [[2018Grant_cisTEM]]
| Review of resolution criteria for electron tomography
| cisTEM
|-  
|-  


| Paper
| Paper
| [[2015Diebolder_ConicalFSC]]
| [[2018McLeod_MRCZ]]
| Conical Fourier Shell Correlation
| MRC Compression format
|-  
|-  


| Paper
| Paper
| [[2020Vilas_Monotomo]]
| [[2018Zivanov_Relion3]]
| Resolution determination in tomograms
| Relion 3
|-
|-  


|}
| Paper
| [[2020Caesar_Simple3]]
| Simple 3
|-


=== Subtomogram analysis ===
| Paper
| [[2021Baldwin_SCF]]
| Visualizer of the Sampling Compensation Factor
|-


{|
| Paper
| [[2021Jimenez_Scipion]]
| Scipion workflow example for image processing
|-


| Paper
| Paper
| [[2000Bohm_Template]]
| [[2021Kimanius_Relion4]]
| Macromolecule finding by template matching
| Changes in Relion 4.0
|-  
|-  


| Paper
| Paper
| [[2002Frangakis_Template]]
| [[2021Maji_BlackBox]]
| Macromolecule finding by template matching
| Exploration of image processing concepts
|-  
|-  


| Paper
| Paper
| [[2006Nickell_Review]]
| [[2021Sharov_Relion]]
| Review of macromolecule finding by template matching (Visual Proteomics)
| Use of Relion within Scipion
|-  
|-  


| Paper
| Paper
| [[2007Best_Review]]
| [[2021Sorzano_Scipion]]
| Review of Localization of Protein Complexes by Pattern Recognition
| Use of Scipion as a way to compare the results of multiple methods
|-  
|-  


| Paper
| Paper
| [[2007Forster_Review]]
| [[2021Strelak_Xmipp]]
| Review of structure determination by subtomogram averaging
| Advances in Xmipp
|-
|-  


| Paper
| Paper
| [[2008Forster_Classification]]
| [[2022DiIorio_Multiple]]
| Classification of subtomograms using constrained correlation
| A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.
|-
|-  


| Paper
| Paper
| [[2008Bartesaghi_Classification]]
| [[2022Fluty_Precision]]
| Classification and averaging of subtomograms
| Precision requirements and data compression
|-
|-  


| Paper
| Paper
| [[2008Schmid_Averaging]]
| [[2022Harastani_ContinuousFlex]]
| Alignment and averaging of subtomograms
| ContinuousFlex: Software for continuous heterogeneity analysis in cryo-EM and cryo-ET
|-
|-  


| Paper
| Paper
| [[2010Amat_Averaging]]
| [[2022Warshamanage_EMDA]]
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
| A Python library for low-level computations such as local correlation
|-
|-  


| Paper
| Paper
| [[2010Yu_PPCA]]
| [[2023Cheng_AutoEMage]]
| Probabilistic PCA for volume classification
| AutoEMage: a system for processing in streaming (SPA)
|-
|-  


| Paper
| Paper
| [[2013Chen_Averaging]]
| [[2023Conesa_Scipion3]]
| Fast alignment of subtomograms using spherical harmonics
| Scipion3: A workflow engine for cryoEM
|-
|-  


| Paper
| Paper
| [[2013Kuybeda_Averaging]]
| [[2023Krieger_ScipionPrody]]
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
| Scipion-EM-Prody: Interface between Scipion and Prody (Structural Analysis)
|-
|-  


| Paper
| Paper
| [[2013Shatsky_Averaging]]
| [[2023Matinyan_TRPX]]
| Alignment and averaging of subtomograms with constrained cross-correlation
| TRPX compression format
|-
|-  


| Paper
| Paper
| [[2013Yu_Projection]]
| [[2023Short_MRC2020]]
| Subtomogram averaging by aligning their projections
| MRC2020: improvements to Ximdisp and the MRC image-processing programs
|-
|-  


| Paper
| Paper
| [[2014Chen_Autofocus]]
| [[2024deLaRosa_EMHub]]
| Subtomogram averaging and classification with special attention to differences
| A web-based Laboratory Information Management System for cryoEM facility
|-
|-  


| Paper
| Paper
| [[2014Yu_ReferenceBias]]
| [[2024Gonzalez_Dashboard]]
| Scoring the reference bias
| A web-based dashboard for Relion
|-
|-  


| Paper
| Paper
| [[2014Voortman_LimitingFactors]]
| [[2024Herre_Capsules]]
| Limiting factors of subtomogram averaging
| SBGrid Capsules to execute programs in controlled environments
|-
|-  


| Paper
| Paper
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| [[2024Moriya_GoToCloud]]
| Subtomogram averaging with CTF correction using a Bayesian prior
| GoToCloud: SPA processing in the cloud
|-  
|-  


| Paper
| Paper
| [[2015Yu_ReferenceBias]]
| [[2024Urzhumtseva_VUE]]
| Scoring the reference bias
| VUE: Visualization of angular distributions
|-
|-  


| Paper
| Paper
| [[2016Bharat_Relion]]
| [[2024Vuillemot_MDSPACE]]
| Subtomogram averaging with Relion
| MDSpace and MDTomo to analyze continuous heterogeneity
|-
|-  


| Paper
| Paper
| [[2016Song_MatrixNorm]]
| [[2025Chen_CryoCRAB]]
| Matrix norm minimization for tomographic reconstruction and alignment
| CryoCRAB: a large database of curated micrographs
|-
|-
 
| Conference
| [[2025Fu_T2Relion]]
| T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
|-  


| Paper
| Paper
| [[2017Castano_ParticlePicking]]
| [[2025Khoshbin_Magellon]]
| Particle picking in tomograms for subtomogram averaging
| Magellon: a software platform for CryoEM image processing
|-
|-  
 
|}


| Paper
== Electron tomography ==
| [[2017Frazier_Tomominer]]
| TomoMiner a software platform for large-scale subtomogram analysis
|-


| Paper
=== Image preprocessing ===
| [[2018Himes_emClarity]]
| emClarity for subtomogram averaging
|-


| Paper
{|
| [[2018Zhao_Fast]]
| Fast alignment and maximum likelihod for subtomogram averaging
|-


| Paper
| Paper
| [[2019Fokine_Enhancement]]
| [[2015Yan_thickness]]
| Subtomogram enhancement through the locked self-rotation
| Determination of thickness, tilt and electron mean free path
|-
|-  


| Paper
| Paper
| [[2019Han_Constrained]]
| [[2018Wu_contrast]]
| Constrained reconstruction to enhance resolution
| Contrast enhancement to improve alignability
|-
|-  
 
|}
 
=== Image alignment ===


| Paper
{|
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-


| Paper
| Paper
| [[2021Cheng_Native]]
| [[1982Guckenberger_commonOrigin]]
| 3D reconstruction only with 0-tilt images
| Determination of a common origin in the micrographs of titl series in three-dimensional electron microscopy
|-
|-  


| Paper
| Paper
| [[2021Du_Active]]
| [[1992Lawrence_leastSquares]]
| Active learning to reduce the need of annotated samples
| Least squares solution of the alignment problem
|-
|-  


| Paper
| Paper
| [[2021Harastani_HEMNMA3D]]
| [[1995Penczek_dual]]
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
| Dual tilt alignment
|-
|-  


| Paper
| Paper
| [[2021Lucas_Cistem]]
| [[1996Owen_alignmentQuality]]
| Identification of particles in tomograms using Cistem
| Automatic alignment without fiducial markers and evaluation of alignment quality
|-
|-  


| Paper
| Paper
| [[2021Scaramuzza_Dynamo]]
| [[1998Grimm_normalization]]
| Subtomogram averaging workflow using Dynamo
| Discussion of several gray level normalization methods for electron tomography
|-
|-  


| Paper
| Paper
| [[2021Singla_Measures]]
| [[2001Brandt_Automatic1]]
| Analysis of different measures to analyze subtomogram clusters
| Automatic alignment without fiducial markers
|-
|-  


| Paper
| Paper
| [[2021Tegunov_M]]
| [[2001Brandt_Automatic2]]
| Image processing workflow for tilt-series (introduction of M)
| Automatic alignment with fiducial markers
|-
|-  


| Conference
| Paper
| [[2021Zeng_OpenSet]]
| [[2006Winkler_alignment]]
| Unsupervised open set classification using deep learning
| Marker-free alignment and refinement
|-
|-  


| Paper
| Paper
| [[2022Boehning_CompressedSensing]]
| [[2006Castano_alignment]]
| Compressed sensing for subtomogram averaging
| Alignment with non-perpendicularity
|-
|-


| Paper
| Paper
| [[2022Hao_Picking]]
| [[2007Castano_alignment]]
| Detection of molecules in tomograms
| Fiducial-less alignment of cryo-sections
|-
|-  


| Paper
| Paper
| [[2022Harastani_TomoFlow]]
| [[2009Sorzano_alignment]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
| Marker-free alignment and refinement
|-
|-  


| Paper
| Paper
| [[2022Metskas_STA]]
| [[2010Cantele_dualAlignment]]
| Tricks for a better Subtomogram Averaging
| Alignment of dual series
|-
|-  


| Paper
| Paper
| [[2022Moebel_unsupervised]]
| [[2014Tomonaga_Automatic]]
| Unsupervised classification of subtomograms using neural networks
| Automatic alignment of tilt series using the projection themselves
|-
|-  


| Paper
| Paper
| [[2022Peters_Feature]]
| [[2014Han_Automatic]]
| Feature guided, focused 3D signal permutation for STA
| Automatic alignment of tilt series using SIFT features
|-
|-  


| Paper
| Paper
| [[2023Balyschew_TomoBEAR]]
| [[2015Han_Automatic]]
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
| Automatic alignment of tilt series using fiducials
|-
|-  


| Paper
| Paper
| [[2023Chaillet_Extensive]]
| [[2017Mastronarde_Automatic]]
| Extensive angular sampling for picking in tomograms
| Automatic alignment and reconstruction of tilt series in IMOD
|-
|-  


| Paper
| Paper
| [[2023Cheng_GisSPA]]
| [[2018Fernadez_Beam]]
| Detection of protein targets in 0-tilt images
| Image alignment considering beam induced motion
|-
|-  


| Paper
| Paper
| [[2023Genthe_PickYolo]]
| [[2018Han_Fast]]
| Subtomogram picking in tomograms
| Automatic alignment using fiducial markers
|-
|-  


| Paper
| Paper
| [[2024Wang_TomoNet]]
| [[2019Fernandez_residual]]
| Subtomogram picking in flexible lattices
| Alignment of tilt series using residual interpolation
|-
|-  


|}
| Paper
| [[2019Han_Dual]]
| Automatic alignment using fiducial markers in dual tilt series
|-


=== Single particle tomography ===
| Paper
 
| [[2020Sorzano_automatic]]
{|
| Automatic alignment considering several geometrical distortions
|-


| Paper
| Paper
| [[2012Bartesaghi_Constrained]]
| [[2021Han_LocalConstraints]]
| 3D reconstruction by imposing geometrical constraints
| Automatic alignment considering local constraints
|-
|-  


| Paper
| Paper
| [[2012Zhang_IPET_FETR]]
| [[2022Ganguly_SparseAlign]]
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
| Sparse Align: Automatic detection of markers and deformation estimation
|-
|-  


| Paper
| Paper
| [[2015Galaz_SingleParticleTomography]]
| [[2022Zheng_Aretomo]]
| Set of tools for Single Particle Tomography in EMAN2
| Automatic alignment based on projection matching
|-  
|-  


| Paper
| Paper
| [[2016Galaz_SingleParticleTomography]]
| [[2024Coray_Automated]]
| Alignment algorithms and CTF correction
| Automated fiducial-based tilt series alignment in Dynamo
|-  
|-  


|}
| Paper
 
| [[2024deIsidro_deep]]
=== Missing-wedge correction ===
| Detection of tilt series misalignment in the reconstructed tomogram using a neural network
 
|-
{|


| Paper
| Paper
| [[2020Kovacs_Filaments]]
| [[2024Hou_Marker]]
| Removal of missing wedge artifacts in filamentous tomograms
| Marker detection using wavelets
|-  
|-  


| Paper
| Paper
| [[2020Moebel_MCMC]]
| [[2024Xu_MarkerAuto2]]
| Missing wedge correction with Monte Carlo Markov Chains
| MarkerAuto2: Tilt series alignment using fiducials
|-
|-  


| Paper
| Paper
| [[2020Zhai_LoTTor]]
| [[2025deIsidro_Misalignment]]
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
| Tilt series misalignment detection
|-
|-  


| Paper
| Paper
| [[2023Zhang_REST]]
| [[2025Guo_Alignment]]
| Missing-wedge correction with neural networks
| Tilt series alignment with L1-norm optimization
|-
|-  


|}
|}


=== Molecular 3D dynamics  ===
=== CTF estimation and restoration ===


{|
{|


| Paper
| Paper
| [[2015Zhang_IPET]]
| [[2003Winkler_CTF]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
| Focus gradient correction in electron tomography
|-
|-  


| Paper
| Paper
| [[2023Vuillemot_MDTOMO]]
| [[2006Fernandez_CTF]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
| CTF determination and correction in electron tomography
|-
|-  


|}


=== Books and reviews ===
| Paper
| [[2009Zanetti_CTF]]
| CTF determination and correction in electron tomography
|-


{|


| Paper
| Paper
| [[2000Baumeister_Review]]
| [[2009Xiong_CTF]]
| Review of electron tomography
| CTF determination and correction for low dose tomographic tilt series
|-  
|-  


| Paper
| Paper
| [[2003Koster_Review]]
| [[2012Eibauer_CTF]]
| Review of electron tomography
| CTF determination and correction
|-  
|-  


| Paper
| Paper
| [[2003Sali_Review]]
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Review of electron tomography
| Subtomogram averaging with CTF correction using a Bayesian prior
|-  
|-  


| Paper
| Paper
| [[2004Henderson_Review]]
| [[2017Turonova_3DCTF]]
| Review of electron microscopy
| 3D CTF Correction
|-  
|-  


| Paper
| Paper
| [[2005Lucic_Review]]
| [[2017Kunz_3DCTF]]
| Review of electron tomography
| 3D CTF Correction
|-  
|-  


| Paper
| Paper
| [[2006Fernandez_Review]]
| [[2024Mastronarded_CTFPlotter]]
| Review of electron microscopy
| CTF estimation with CTFPlotter
|-  
|-  


| Book
| Paper
| [[2006Frank_TomoBook]]
| [[2024Zhang_CTFMeasure]]
| Electron Tomography
| Simultaneous CTF estimation for a whole tilt series
|-  
|-  


| Book
| Paper
| [[2007McIntosh_Book]]
| [[2025Khavnekar_PSD]]
| Cellular Electron Microscopy
| Accurate PSD determination in tilt series
|-  
|-  
|}
=== 3D reconstruction ===
{|


| Paper
| Paper
| [[2007Sorzano_Review]]
| [[1972Gilbert_SIRT]]
| Review of the image processing steps
| Simultaneous Iterative Reconstruction Technique (SIRT)
|-  
|-  


| Paper
| Paper
| [[2008Fanelli_ImageFormation]]
| [[1973Herman_ART]]
| Review on the image formation model from the electron waves and open inverse-problems
| Algebraic Reconstruction Technique (ART)
|-  
|-  


| Paper
| Paper
| [[2008Fernandez_HPCReview]]
| [[1984Andersen_SART]]
| High performance computing in electron cryomicroscopy
| Simultaneous Algebraic Reconstruction Technique (SART)
|-  
|-  


| Paper
| Paper
| [[2008Jonic_Review]]
| [[1992Radermacher_WBP]]
| Comparison between electron tomography and single particles
| Weighted Backprojection in electron tomography
|-  
|-  


| Paper
| Paper
| [[2012Kudryashev_Review]]
| [[1997Marabini_reconstruction]]
| Review of subtomogram averaging
| Iterative reconstruction in electron tomography
|-  
|-  


| Paper
| Paper
| [[2013Briggs_Review]]
| [[2002Fernandez_reconstruction]]
| Review of subtomogram averaging
| Iterative reconstruction in electron tomography
|-  
|-  


| Paper
| Paper
| [[2016Beck_Review]]
| [[2007Radermacher_WBP]]
| Review of molecular sociology
| Weighted Backprojection in electron tomography
|-  
|-  


| Paper
| Paper
| [[2016Ercius_Review]]
| [[2008Fernandez_CARP]]
| Electron tomography for hard and soft materials research
| Component Averaged Row Projections (CARP)
|-  
|-  


| Paper
| Paper
| [[2017Galaz_Review]]
| [[2010Xu_Long]]
| Review of single particle tomography
| Iterative reconstructions with long object correction and GPU implementation
|-  
|-  


| Paper
| Paper
| [[2017Plitzko_Review]]
| [[2012Herman General Superiorization]]
| Review of electron tomography, FRET and FIB milling
| Superiorization: an optimization heuristic for medical physics
|-  
|-  


| Paper
| Paper
| [[2019Schur_Review]]
| [[2012Zhang_IPET_FETR]]
| Review of electron tomography and subtomogram averaging
| IPET and FETR, a reconstruction algorithm for a single particle structure determination without any averaging
|-  
|-  


| Paper
| Paper
| [[2021Frangakis_Review]]
| [[2013Goris_SIRT_TV_DART]]
| Review of tomogram denoising in electron tomography
| Combination of SIRT, Total Variation and Discrete ART to reconstruct and segment at the same time
|-  
|-  


| Paper
| Paper
| [[2022Forster_Review]]
| [[2013Briegel A_Challenge]]
| Review of subtomogram averaging
| The challenge of determining handedness in electron tomography and the use of DNA origami gold nanoparticle helices as molecular standards
|-  
|-  


| Paper
| Paper
| [[2022Liedtke_Review]]
| [[2013Messaoudi_EnergyFiltered]]
| Review of electron tomography in bacterial cell biology
| 3D Reconstruction of Energy-Filtered TEM
|-  
|-  


| Paper
| Paper
| [[2022Liu_Review]]
| [[2014Paavolainen_Missing]]
| Review of beam image shift and subtomogram averaging
| Compensation of the missing wedge
|-  
|-  


| Paper
| Paper
| [[2023Kim_Review]]
| [[2015Venkatakrishnan_MBIR]]
| Review of particle picking and volume segmentation
| 3D Reconstruction with priors
|-  
|-  


| Paper
| Paper
| [[2023Ochner_Review]]
| [[2016Deng_ICON]]
| Review of electron tomography as a way to visualize macromolecules in their native environment
| 3D Reconstruction with missing information restoration
|-  
|-  


| Paper
| Paper
| [[2023Zhao_Review]]
| [[2016Guay_Compressed]]
| Review of computational methods for electron tomography
| 3D Reconstruction using compressed sensing
|-  
|-  


|}
| Paper
 
| [[2016Turonova_Artifacts]]
=== Software ===
| Artifacts observed during 3D reconstruction
 
|-
{|


| Paper
| Paper
| [[1996Kremer_IMOD]]
| [[2019Yan_MBIR]]
| IMOD
| 3D Reconstruction with priors and demonstration of its use in biological samples
|-  
|-  


| Paper
| Paper
| [[1996Chen_Priism/IVE]]
| [[2020Sanchez_Hybrid]]
| Priism/IVE
| 3D reconstruction with a special acquisition and alignment scheme
|-  
|-  


| Paper
| Paper
| [[1996Frank_Spider]]
| [[2020Song_Tygress]]
| Spider
| 3D reconstruction with a special acquisition and alignment scheme
|-  
|-  


| Paper
| Paper
| [[2004Sorzano_Xmipp]]
| [[2021Fernandez_TomoAlign]]
| Xmipp
| 3D reconstruction with sample motion and CTF correction
|-  
|-


| Paper
| Paper
| [[2005Nickell_TOM]]
| [[2021Geng_Nudim]]
| TOM Toolbox
| Non-uniform FFT reconstruction and total variation to fill the missing wedge
|-  
|-


| Paper
| Paper
| [[2007Messaoudi_TomoJ]]
| [[2024vanVeen_Missing]]
| TomoJ
| Missing wedge filling in cryoET
|-  
|-


| Paper
| Paper
| [[2008Heymann_BsoftTomo]]
| [[2025Debarnot_IceTide]]
| Bsoft
| 3D Reconstruction in CryoET with local deformation corrections and neural networks
|-  
|-
 
|}
 
=== Noise reduction ===
{|


| Paper
| Paper
| [[2012Zhang IPET FETR]]
| [[2001Frangakis_NAD]]
| IPET
| Noise reduction with Nonlinear Anisotropic Diffusion
|-  
|-  


| Paper
| Paper
| [[2015Ding_CaltechTomography]]
| [[2003Fernandez_AND]]
| Caltech tomography database
| Anisotropic nonlinear diffusion for electron tomography
|-  
|-  


| Paper
| Paper
| [[2015Noble_AppionProtomo]]
| [[2003Jiang_Bilateral]]
| Batch fiducial-less tilt-series alignment in Appion using Protomo
| Bilateral denoising filter in electron microscopy
|-  
|-  


| Paper
| Paper
| [[2015vanAarle_Astra]]
| [[2005Fernandez_AND]]
| ASTRA Toolbox
| Anisotropic nonlinear denoising in electron tomography
|-  
|-  


| Paper
| Paper
| [[2016Liu_FullMechTomo]]
| [[2007Heide_median]]
| Fully mechanically controlled automated electron microscopic tomography
| 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
| Paper
| [[2017Han_AuTom]]
| [[2014Kovacik_wedgeArtefacts]]
| Software platform for Electron Tomography
| Removal of wedge artefacts
|-  
|-  


| Paper
| Paper
| [[2017Wan_Simulator]]
| [[2014Maiorca_beadArtefacts]]
| Electron Tomography Simulator
| Removal of gold bead artefacts
|-  
|-  


| Paper
| Paper
| [[2021Burt_RWD]]
| [[2018Trampert_Inpainting]]
| Interoperability between Relion, Warp M, and Dynamo
| Removal of the missing wedge by inpainting
|-  
|-  


| Paper
| Paper
| [[2022Jimenez_ScipionTomo]]
| [[2018Moreno_TomoEED]]
| Electron tomography within Scipion
| Fast Anisotropic Diffusion
|-  
|-  


| Paper
| Paper
| [[2022Martinez_PyOrg]]
| [[2018Wu_Enhancement]]
| Point pattern analysis for coordinates in tomograms
| Enhancing the image contrast of electron tomography
|-  
|-  


| Paper
| Paper
| [[2022Ni_EmClarity]]
| [[2022Liu_Isonet]]
| Processing protocols with EmClarity
| Isotropic reconstructions using deep learning
|-  
|-  


| Paper
| Paper
| [[2023Liu_NextPYP]]
| [[2024vanBlerkom_GoldX]]
| NextPYP: a software platform for cryoET
| GoldX: Gold bead removal
|-  
|-  


| Paper
| Paper
| [[2024Horstmann_PATo]]
| [[2025Costa_CryoSamba]]
| PATo: web application for cryoET processing in streaming
| CryoSamba: tomogram denoising
|-  
|-  


|}
|}


== 2D Crystals ==
=== Segmentation ===
 
=== 2D Preprocessing ===


{|
{|


| Paper
| Paper
| [[1982Saxton_Averaging]]
| [[2002Frangakis_Eigenanalysis]]
| Radial Correlation Function
| Segmentation using eigenvector analysis.
|-  
|-  


| Paper
| Paper
| [[1984Saxton_Distortions]]
| [[2002Volkmann_Watershed]]
| 3D Reconstruction of distorted crystals
| Segmentation using watershed transform.
|-  
|-  


| Paper
| Paper
| [[1986Henderson_Processing]]
| [[2003Bajaj_BoundarySegmentation]]
| General 2D processing
| Segmentation based on fast marching.
|-  
|-


| Paper
| Paper
| [[2000He_PhaseAlignment]]
| [[2005Cyrklaff_Thresholding]]
| Phase consistency and Alignment
| Segmentation using optimal thresholding.
|-  
|-  


| Paper
| Paper
| [[2006Gil_Unbending]]
| [[2007Lebbink_TemplateMatching]]
| Crystal unbending
| Segmentation using template matching.
|-  
|-  
|}
=== Classification ===
{|


| Paper
| Paper
| [[1988Frank_Classification]]
| [[2007Sandberg_OrientationFields]]
| MSA and classification in electron crystallography
| Segmentation using orientation fields.
|-  
|-  


| Paper
| Paper
| [[1996Fernandez_SOM]]
| [[2007Sandberg_SegmentationReview]]
| Classification based on self organizing maps
| Review on segmentation in electron tomography.
|-  
|-  


| Paper
| Paper
| [[1998Sherman_MSA]]
| [[2008Garduno_FuzzySegmentation]]
| Classification based on MSA
| Segmentation using fuzzy set theory principles.
|-  
|-  
|}
=== 3D Reconstruction ===
{|


| Paper
| Paper
| [[1985Wang_Solvent]]
| [[2009Lebbink_TemplateMatching2]]
| Solvent flattening
| Segmentation using template matching.
|-  
|-  


| Paper
| Paper
| [[1990Henderson_Processing]]
| [[2012RubbiyaAli_EdgeDetection]]
| General 3D processing
| Parameter-Free Segmentation of Macromolecular Structures.
|-
|-  


| Paper
| Paper
| [[2004Marabini_ART]]
| [[2014Martinez-Sanchez_TomoSegMemTV]]
| Algebraic Reconstruction Technique with blobs for crystals (Xmipp)
| Membrane segmentation.
|-
 
| Conference
| [[2015Xu_TemplateMatching]]
| Detection of macromolecular complexes with a reduced representation of the templates.
|-  
|-  


| Paper
| Paper
| [[2018Biyani_Badlu]]
| [[2017Ali_RAZA]]
| Image processing for badly ordered crystals
| Automated segmentation of tomograms
|-  
|-  


|}
| Paper
| [[2017Chen_Annotation]]
| Automated annotation of tomograms
|-


=== Books and reviews ===
| Paper
 
| [[2017Tasel_ActiveContours]]
{|
| Segmentation with active contours
|-


| Paper
| Paper
| [[1998Walz_Review]]
| [[2017Xu_DeepLearning]]
| Review of 2D crystallography
| Finding proteins in tomograms using deep learning
|-  
|-  


| Paper
| Paper
| [[1999Glaeser_Review]]
| [[2018Zeng_DeepLearning]]
| Review of 2D crystallography
| Mining features in Electron Tomography by deep learning
|-  
|-  


| Paper
| Paper
| [[2001Ellis_Review]]
| [[2020Salfer_PyCurv]]
| Review of 2D crystallography
| Curvature analysis of segmented tomograms
|-  
|-  


| Paper
| Paper
| [[2001Glaeser_Review]]
| [[2021Dimchev_filaments]]
| Review of 2D crystallography
| Segmentation of filaments in tomograms
|-  
|-  


| Paper
| Paper
| [[2004Henderson_Review]]
| [[2022Frangakis_Curvature]]
| Review of electron microscopy
| Use of mean curvature for segmentation and visualization of tomograms
|-  
|-  


| Paper
| Paper
| [[2006Fernandez_Review]]
| [[2022Lamm_MemBrain]]
| Review of single particles, electron tomography and crystallography
| Membrane segmentation using deep learning
|-  
|-  


| Paper
| Paper
| [[2007Sorzano_Review]]
| [[2023Sazzed_Struwwel]]
| Review of the image processing steps
| Detection and analysis of filament networks
|-  
|-  


|}
| Paper
| [[2023Zeng_AITOM]]
| Structural pattern mining by unsupervised deep iterative subtomogram clustering
|-


=== Software ===
| 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
| Paper
| [[1996Crowther_MRC]]
| [[2024Last_Ais]]
| MRC
| Ais: Interactive segmentation of tomograms
|-  
|-  


| Paper
| Paper
| [[2004Sorzano_Xmipp]]
| [[2024Siggel_ColabSeg]]
| Xmipp
| Interactive membrane segmentation of tomograms
|-  
|-  


| Paper
| Paper
| [[2007Gipson_2dx]]
| [[2025Chen_GCTransNet]]
| 2dx
| GCTransNet: Segmentation of mitochondrias in volume electron microscopy
|-  
|-  


| Paper
| Paper
| [[2007Heymann_Bsoft]]
| [[2025Morales_Membranes]]
| Bsoft
| Membrane segmentation with a neural network
|-  
|-  


| Paper
| Paper
| [[2007Philippsen_IPLT]]
| [[2025Schoennenbeck_CryoVIA]]
| IPLT
| CryoVIA: An image analysis toolkit for the quantification of membrane structures
|-  
|-  


|}
|}


== 3D Crystals - MicroED ==
=== Resolution ===
{|


=== Sample Preparation ===
{|
| Paper
| Paper
| [[2016Shi_Preparation]]
| [[2005Cardone_Resolution]]
| Sample Preparation
| Resolution criterion for electron tomography
|}
|-
=== Data Collection ===
{|
| Paper
| [[2014Nannenga_CR]]
| Continuous rotation


|}
| Chapter
=== Data Processing ===
| [[2007Penczek_Resolution]]
{|
| Review of resolution criteria for electron tomography
|-


| Paper
| Paper
| [[2011Wisedchaisri_PhaseExtension]]
| [[2015Diebolder_ConicalFSC]]
| Fragment-based phase extension
| Conical Fourier Shell Correlation
|-
|-  


| Paper
| Paper
| [[2015Hattne_Processing]]  
| [[2020Vilas_Monotomo]]
| Data Processing
| Resolution determination in tomograms
|-
|-
 
| Paper
| [[2016Hattne_Correction]]
| Image correction
|}
|}


=== Software ===
=== Subtomogram analysis ===
{|


| Paper
| [[2014Iadanza_Processing]]
| Data Processing of still diffraction data
|}
=== Books and Reviews ===
{|
{|
|  Paper
| [[2014Nannenga_Review ]]
| Review of MicroED
|-


| Paper
| Paper
| [[2016Liu_Review ]]
| [[2000Bohm_Template]]
| Review of MicroED
| Macromolecule finding by template matching
|-
|-  


| Paper
| Paper
| [[2016Rodriguez_Review ]]
| [[2002Frangakis_Template]]
| Review of MicroED
| Macromolecule finding by template matching
|-  
|-  
|}
== Helical particles ==
=== Filament picking ===
{|


| Paper
| Paper
| [[2021Thurber_Automated]]
| [[2006Nickell_Review]]
| Automated picking of filaments
| Review of macromolecule finding by template matching (Visual Proteomics)
|-  
|-  


| Paper
| Paper
| [[2023Li_Classification]]
| [[2007Best_Review]]
| Classification of filament segments using language models
| Review of Localization of Protein Complexes by Pattern Recognition
|-  
|-  


|}
| Paper
| [[2007Forster_Review]]
| Review of structure determination by subtomogram averaging
|-


=== Filament corrections ===
| Paper
| [[2008Forster_Classification]]
| Classification of subtomograms using constrained correlation
|-


{|
| Paper
| [[2008Bartesaghi_Classification]]
| Classification and averaging of subtomograms
|-


| Paper
| Paper
| [[1986Egelman_Curved]]
| [[2008Schmid_Averaging]]
| Algorithm for correcting curved filaments
| Alignment and averaging of subtomograms
|-  
|-


| Paper
| Paper
| [[1988Bluemke_Pitch]]
| [[2010Amat_Averaging]]
| Algorithm for correcting filaments with different helical pitches
| Alignment and averaging of subtomograms exploiting thresholding in Fourier space
|-  
|-


| Paper
| Paper
| [[2006Wang_Pitch]]
| [[2010Yu_PPCA]]
| Algorithm for correcting filaments with different helical pitches
| Probabilistic PCA for volume classification
|-  
|-


| Paper
| Paper
| [[2016Yang_Flexible]]
| [[2013Chen_Averaging]]
| Algorithm for correcting filaments with flexible subunits
| Fast alignment of subtomograms using spherical harmonics
|-  
|-


| Paper
| Paper
| [[2019Ohashi_SoftBody]]
| [[2013Kuybeda_Averaging]]
| Algorithm for correcting filaments with flexible helices
| Alignment and averaging of subtomograms using the nuclear norm of the cluster
|-  
|-
 
|}
 
=== Reconstruction ===
 
{|


| Paper
| Paper
| [[1952Cochran_Fourier]]
| [[2013Shatsky_Averaging]]
| Fourier Bessel transform of filamentous structures
| Alignment and averaging of subtomograms with constrained cross-correlation
|-  
|-


| Paper
| Paper
| [[1958Klug_Fourier]]
| [[2013Yu_Projection]]
| Fourier Bessel decomposition of the projection images
| Subtomogram averaging by aligning their projections
|-  
|-


| Paper
| Paper
| [[1970DeRosier_Rec]]
| [[2014Chen_Autofocus]]
| Image processing steps towards 3D reconstruction
| Subtomogram averaging and classification with special attention to differences
|-  
|-


| Paper
| Paper
| [[1988Stewart_Rec]]
| [[2014Yu_ReferenceBias]]
| Image processing steps towards 3D reconstruction
| Scoring the reference bias
|-  
|-


| Paper
| Paper
| [[1992Morgan_Rec]]
| [[2014Voortman_LimitingFactors]]
| Image processing steps towards 3D reconstruction
| Limiting factors of subtomogram averaging
|-  
|-


| Paper
| Paper
| [[2005Wang_Iterative]]
| [[2015Bharat_CTFCorrectedSubtomogramAveraging]]
| Iterative Fourier-Bessel algorithm
| Subtomogram averaging with CTF correction using a Bayesian prior
|-  
|-  


| Paper
| Paper
| [[2007Egelman_Iterative]]
| [[2015Yu_ReferenceBias]]
| Iterative real-space algorithm
| Scoring the reference bias
|-  
|-


| Paper
| Paper
| [[2010Egelman_Pitfalls]]
| [[2016Bharat_Relion]]
| Pitfalls in helical reconstruction
| Subtomogram averaging with Relion
|-  
|-


| Paper
| Paper
| [[2013Desfosses_Spring]]
| [[2016Song_MatrixNorm]]
| Helical processing with Spring
| Matrix norm minimization for tomographic reconstruction and alignment
|-  
|-


| Paper
| Paper
| [[2015Zhang_seam]]
| [[2017Castano_ParticlePicking]]
| Workflow for the detection of the lattice seam
| Particle picking in tomograms for subtomogram averaging
|-  
|-


| Paper
| Paper
| [[2016Rohou_Frealix]]
| [[2017Frazier_Tomominer]]
| Helical processing with Frealix
| TomoMiner a software platform for large-scale subtomogram analysis
|-  
|-


| Paper
| Paper
| [[2017_He]]
| [[2018Himes_emClarity]]
| Helical processing with Relion
| emClarity for subtomogram averaging
|-  
|-


| Paper
| Paper
| [[2019_Pothula]]
| [[2018Zhao_Fast]]
| 3D Classification through 2D analysis
| Fast alignment and maximum likelihod for subtomogram averaging
|-  
|-


|}
| Paper
| [[2019Fokine_Enhancement]]
| Subtomogram enhancement through the locked self-rotation
|-


=== Validation ===
| Paper
| [[2019Han_Constrained]]
| Constrained reconstruction to enhance resolution
|-


{|
| Paper
| [[2020Basanta_workflow]]
| Workflow for subtomogram averaging
|-


| Paper
| Paper
| [[2014Egelman_ambiguity]]
| [[2020Zeng_GumNet]]
| How to detect incorrect models
| GumNet: Subtomogram averaging using deep learning
|-  
|-
 
|}
 
=== Books and reviews ===
 
{|


| Paper
| Paper
| [[1970DeRosier_Rec]]
| [[2021Cheng_Native]]
| Image processing steps towards 3D reconstruction
| 3D reconstruction only with 0-tilt images
|-  
|-


| Paper
| Paper
| [[1992Morgan_Rec]]
| [[2021Du_Active]]
| Image processing steps towards 3D reconstruction
| Active learning to reduce the need of annotated samples
|-  
|-


| Paper
| Paper
| [[2004Henderson_Review]]
| [[2021Harastani_HEMNMA3D]]
| Review of electron microscopy
| HEMNMA-3D: Continuous flexibility analysis of subtomograms using normal modes
|-  
|-


| Paper
| Paper
| [[2015Sachse_Review]]
| [[2021Lucas_Cistem]]
| Review of the image processing steps in helical particles
| Identification of particles in tomograms using Cistem
|-  
|-


| Paper
| Paper
| [[2021Egelman_Review]]
| [[2021Moebel_DeepFinder]]
| Review of reconstruction problems in helical structures
| DeepFinder: Identification of particles in tomograms using neural networks
|-  
|-


| Paper
| Paper
| [[2022Wang_Review]]
| [[2021Scaramuzza_Dynamo]]
| Review of reconstruction problems in helical structures
| Subtomogram averaging workflow using Dynamo
|-  
|-


|}
| Paper
| [[2021Singla_Measures]]
| Analysis of different measures to analyze subtomogram clusters
|-


=== Software ===
| Paper
| [[2021Tegunov_M]]
| Image processing workflow for tilt-series (introduction of M)
|-


{|
| Conference
| [[2021Zeng_OpenSet]]
| Unsupervised open set classification using deep learning
|-


| Paper
| Paper
| [[1996Carragher_Phoelix]]
| [[2022Bandyopadhyay_Adaptation]]
| Phoelix
| Cryo-Shift: a neural network to bridge the gap between simulated and experimental data
|-  
|-


| Paper
| Paper
| [[1996Crowther_MRC]]
| [[2022Boehning_CompressedSensing]]
| MRC
| Compressed sensing for subtomogram averaging
|-  
|-


| Paper
| Paper
| [[1996Owen_Brandeis]]
| [[2022Hao_Picking]]
| Brandeis
| Detection of molecules in tomograms
|-  
|-


|}
| Paper
| [[2022Harastani_TomoFlow]]
| TomoFlow: Continuous flexibility analysis of subtomograms using 3D dense optical flow
|-


== Icosahedral particles ==
| Paper
| [[2022Metskas_STA]]
| Tricks for a better Subtomogram Averaging
|-


=== Reconstruction ===
| Paper
| [[2022Moebel_unsupervised]]
| Unsupervised classification of subtomograms using neural networks
|-


{|
| Paper
| [[2022Peters_Feature]]
| Feature guided, focused 3D signal permutation for STA
|-


| Paper
| Paper
| [[1970Crowther_Rec]]
| [[2023Balyschew_TomoBEAR]]
| Reconstruction of icosahedral viruses in Fourier space
| TomoBEAR: tilt series alignment, reconstruction and subtomogram averaging
|-  
|-


| Paper
| Paper
| [[1971Crowther_Rec]]
| [[2023Chaillet_Extensive]]
| Reconstruction of icosahedral viruses in Fourier space
| Extensive angular sampling for picking in tomograms
|-  
|-


| Paper
| Paper
| [[1996Fuller_Rec]]
| [[2023Cheng_GisSPA]]
| Reconstruction of icosahedral viruses in Fourier space
| Detection of protein targets in 0-tilt images
|-  
|-


| Paper
| Paper
| [[1997Thuman_Rec]]
| [[2023Genthe_PickYolo]]
| Reconstruction of icosahedral viruses in Fourier space
| Subtomogram picking in tomograms
|-  
|-


| Paper
| Paper
| [[2019Goetschius_Asymmetric]]
| [[2023Rice_TomoTwin]]
| Approaches to reconstruct asymmetric features in viruses
| Subtomogram picking in tomograms
|-  
|-


|}
| Paper
| [[2024Almira_TTM]]
| Theory of the Tensor Template matching for cryoET
|-


=== Classification ===
| Paper
| [[2024Cruz_Template]]
| Template matching for cryoET
|-


{|
| Paper
| [[2024Huang_MiLoPYP]]
| Self-supervised particle localization in tomograms
|-


| Paper
| Paper
| [[2005Scheres_Virus]]
| [[2024Jin_Size]]
| Classification of virus capsids in real space
| Subtomogram picking based on size
|-  
|-


|}
| Paper
| [[2024Karimi_Vesicle]]
| Picking of particles embedded in vesicles
|-


=== Books and reviews ===
| Paper
| [[2024Liu_DeepETPicker]]
| DeepETPicker, subtomogram picker using deep learning
|-


{|
| Paper
| [[2024Powell_TomoDRGN]]
| TomoDRGN: continuous heterogeneity in subtomograms
|-


| Paper
| Paper
| [[1999Baker_Review]]
| [[2024Rangan_CryoDRGNET]]
| Review of reconstruction of icosahedral viruses
| CryoDRGN-ET: heterogeneity analysis for subtomograms
|-  
|-


| Paper
| Paper
| [[1999Conway_Review]]
| [[2024Wan_StopGap]]
| Review of reconstruction of icosahedral viruses
| StopGap: program to locate, align and classify subtomograms
|-  
|-


| Paper
| Paper
| [[2000Thuman_Review]]
| [[2024Wang_TomoNet]]
| Review of reconstruction of icosahedral viruses
| Subtomogram picking in flexible lattices
|-  
|-


| Paper
| Paper
| [[2003Lee_Review]]
| [[2025Chaillet_PytomMatchPick]]
| Review of reconstruction of icosahedral viruses
| pytom-match-pick: particle picking in tomograms
|-  
|-


| Paper
| Paper
| [[2003Navaza_Review]]
| [[2025Shah_TomoCPT]]
| Review of reconstruction of icosahedral viruses
| TomoCPT: particle picking in tomograms
|-  
|-


| Paper
| Paper
| [[2006Grunewald_Review]]
| [[2025Yan_MPicker]]
| Review of reconstruction of icosahedral viruses
| Membrane protein picking in electron tomograms
|-  
|-


|}
|}


=== Software ===
=== Single particle tomography ===


{|
{|


| Paper
| Paper
| [[1996Baker_EMPFT]]
| [[2012Bartesaghi_Constrained]]
| EMPFT
| 3D reconstruction by imposing geometrical constraints
|-  
|-


| Paper
| Paper
| [[1996Crowther_MRC]]
| [[2012Zhang_IPET_FETR]]
| MRC
| FETR: a focused reconstruction algorithm for a single molecule 3D structure determination without any averaging
|-  
|-
 
 
| Paper
| Paper
| [[1996Frank_Spider]]
| [[2015Galaz_SingleParticleTomography]]
| Spider
| Set of tools for Single Particle Tomography in EMAN2
|-  
|-
 
 
| Paper
| Paper
| [[1996VanHeel_Imagic]]
| [[2016Galaz_SingleParticleTomography]]
| Imagic
| Alignment algorithms and CTF correction
|-  
|-
 
 
| Paper
|}
| [[2004Sorzano_Xmipp]]
 
| Xmipp
=== Missing-wedge correction ===
|-  
 
 
{|
| Paper
 
| [[2013DeLaRosa_Xmipp30]]
| Paper
| Xmipp 3.0
| [[2020Kovacs_Filaments]]
|-  
| Removal of missing wedge artifacts in filamentous tomograms
 
|-
| Paper
 
| [[2013Morin_Sliz SBGrid]]
| Paper
| SBGrid presentation for eLife  
| [[2020Moebel_MCMC]]
|-  
| Missing wedge correction with Monte Carlo Markov Chains
 
|-
|}
 
 
| Paper
== Liquid-cell TEM / in-situ TEM ==
| [[2020Zhai_LoTTor]]
 
| Missing-wedge correction by LoTTor ('''Lo'''w-'''T'''ilt '''T'''omographic 3D '''R'''econstruction for a single molecule structure)
 
|-
{|
 
 
| Paper
| Paper
| [[2023Zhang_REST]]
| [[2020Ren_LTEM]]
| Missing-wedge correction with neural networks
| Real-time dynamic imaging of sample in liquid phase
|-
|-  
 
 
| Paper
|}
| [[2025Kiewisz_ProjectionSynthesis]]
 
| Projection synthesis of electron tomography data using neural networks
== Databases ==
|-
 
 
{|
|}
 
 
| Paper
=== Molecular 3D dynamics  ===
| [[2003Boutselakis_EMSD]]
 
| EMSD database
{|
|-  
 
 
| Paper
| Paper
| [[2015Zhang_IPET]]
| [[2005Heymann_Conventions]]
| 3D Structural Dynamics of Macromolecules by individual-particle structures without averaging
| Conventions for software interoperability
|-
|-  
 
 
| Paper
| Paper
| [[2023Vuillemot_MDTOMO]]
| [[2005Heymann_Conventions]]
| 3D Structural Dynamics of using molecular dynamics and normal modes
| Conventions for software interoperability
|-
|-  
 
 
|}
| Paper
 
| [[2011Kim_CDDB]]
=== Books and reviews ===
| Conformational Dynamics Data Bank
 
|-  
{|
 
| 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
|}
 
=== 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
|-
 
|}
 
=== 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
|-
 
|}
 
=== 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
|-
 
|}
 
=== 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
| Paper
Line 6,604: Line 7,788:
| [[2018wwwPDB_PDB]]
| [[2018wwwPDB_PDB]]
| Review of PDB advances
| Review of PDB advances
|-
| Paper
| [[2021Nair_PDBe]]
| PDBe API
|-  
|-  


Line 6,619: Line 7,808:
| [[2024Kleywegt_ArchivingValidation]]
| [[2024Kleywegt_ArchivingValidation]]
| Community recommendations for archival and validation
| 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
|-  
|-  


Revision as of 13:46, 12 December 2025

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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== Abstract ==

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== Links ==

== Related software ==

== Related methods ==

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Electron microscopy images

Useful resources

Map of cryoEM microscopes and labs in the world

CryoEM genealogy

Online courses and Learning material

Caltech (same course in Coursera) (latest version of the course in EM-learning)

MRC

MRC training channel

CNB-CSIC

Workshop on Computational methods for CryoEM

Workshop on Management of large CryoEM facilities

Icknield Course on Model Building and Refinement for High Resolution EM Maps

Tutorial on how to prepare negative staining samples

Tutorial on how to prepare samples

Do's and don'ts on sample preparation

NRAMM Workshop 2017 (course slides)

SBGrid videos about the programs they offer

Madrid course on single particle analysis

CCP-EM Spring symposium 2019

CCP-EM Spring symposium 2020

NCCAT Single Particle short course 2020

Cell atlas book by Grant Jensen and Catherine Oikonomou

Purdue CryoEM Virtual Reality Augmented Training

NCCAT Short course on Tomography

Map with CryoEM Facilities

NCCAT Single Particle Analysis short course

Algorithms for Structural Bioinformatics, AlgoSB2023, Cargese

One world CryoEM technical talks

Cryo-EMAcademy YouTube

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 2013Shigematsu H_Noisemodels Noise models and cryo-EM drift correction with a direct-electron camera
Paper 2013Li X K2 noisemodels Influence of electron dose rate on electron counting images recorded with the K2 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 2025Wu_ZeroLossCCCorrected Imaging with chromatic aberration correction and zero loss electrons

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 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 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

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

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

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

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

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

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 2025Van_Probabilistic Multireference initial volume reconstruction in SPA
Paper 2025Woollard_InstaMap InstaMap: 3D reconstruction using neural networks

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

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

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_GMMs Model building in heterogeneous maps
Paper 2025Haloi_Ligand Ligand detection in CryoEM maps using structure prediction and flexible fitting
Paper 2025Karolczak_Ligand Ligand detection in CryoEM maps using deep learning
Paper 2025Luo_DiffFit DiffFit: Flexible fitting of map and atomic model
Paper 2025Mallet_crAI crAI: detection of antibodies in cryoEM maps
Paper 2025Matsuoka_ForceConstant Empirical determination of the force constant for flexible fitting
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

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 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 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

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 2025Chen_CryoCRAB CryoCRAB: a large database of curated micrographs
Conference 2025Fu_T2Relion T2-Relion: Task-parallelism, Tensor-core acceleration of Relion
Paper 2025Khoshbin_Magellon Magellon: a software platform for CryoEM image processing

Electron tomography

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

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

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 (Low-Tilt Tomographic 3D Reconstruction 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

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

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

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

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
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.

Carlos Oscar S. Sorzano: CSIC, Madrid, Spain

Cédric Messaoudi: Institute Curie, Paris, France

Javier Vargas:: CSIC, Madrid, Spain

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

Tamir Gonen: Howard Hughes Medical Institute, Ashburn, VA, USA

Tomas Majtner: Biocomputing Unit CNB-CSIC, Madrid, Spain

[Vahid Abrishami]: CSIC, Madrid, Spain

Gang (Gary) Ren: The Molecular Foundry, LBNL, USA

3DEM sites

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