WikiSysop: Created page with "== Citation == Costa-Gomes, B., Greer, J., Juraschko, N., Parkhurst, J., Mirecka, J., Famili, M., Rangel-Smith, C., Strickson, O., Lowe, A., Basham, M. and others 2025. PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning. Structural Biology and Crystallization Communications. 81, 10 (2025). == Abstract == Ease of access to data, tools and models expedites scientific research. In structural biolo..."

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Created page with "== Citation == Costa-Gomes, B., Greer, J., Juraschko, N., Parkhurst, J., Mirecka, J., Famili, M., Rangel-Smith, C., Strickson, O., Lowe, A., Basham, M. and others 2025. PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning. Structural Biology and Crystallization Communications. 81, 10 (2025). == Abstract == Ease of access to data, tools and models expedites scientific research. In structural biolo..."

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== Citation ==

Costa-Gomes, B., Greer, J., Juraschko, N., Parkhurst, J., Mirecka, J., Famili, M., Rangel-Smith, C., Strickson, O., Lowe, A., Basham, M. and others 2025. PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning. Structural Biology and Crystallization Communications. 81, 10 (2025).

== Abstract ==

Ease of access to data, tools and models expedites scientific research. In
structural biology there are now numerous open repositories of experimental
and simulated data sets. Being able to easily access and utilize these is crucial
to allow researchers to make optimal use of their research effort. The tools
presented here are useful for collating existing public cryoEM data sets and/or
creating new synthetic cryoEM data sets to aid the development of novel data
processing and interpretation algorithms. In recent years, structural biology has
seen the development of a multitude of machine-learning-based algorithms to
aid numerous steps in the processing and reconstruction of experimental data
sets and the use of these approaches has become widespread. Developing such
techniques in structural biology requires access to large data sets, which can be
cumbersome to curate and unwieldy to make use of. In this paper, we present a
suite of Python software packages, which we collectively refer to as PERC
(profet, EMPIARreader and CAKED). These are designed to reduce the burden
which data curation places upon structural biology research. The protein
structure fetcher (profet) package allows users to conveniently download and
cleave sequences or structures from the Protein Data Bank or AlphaFold
databases. EMPIARreader allows lazy loading of Electron Microscopy Public
Image Archive data sets in a machine-learning-compatible structure. The Class
Aggregator for Key Electron-microscopy Data (CAKED) package is designed
to seamlessly facilitate the training of machine-learning models on electron
microscopy data, including electron-cryo-microscopy-specific data augmentation
and labeling. These packages may be utilized independently or as building
blocks in workflows. All are available in open-source repositories and designed
to be easily extensible to facilitate more advanced workflows if required.

== Keywords ==

== Links ==

https://journals.iucr.org/f/issues/2025/10/00/ir5046/index.html

== Related software ==

== Related methods ==

== Comments ==

2025Costa PERC - Revision history