<i>CryoSift</i> : an accessible and automated CNN-driven tool for cryo-EM 2D class selection
Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge in cryo-EM data-set processing. Here, we present a platform-independent convolutional neural network (CNN) tool for assessing the quality of 2D averages to enable the automatic selection of suitable particles for high-resolution reconstructions, termed CryoSift. We integrate CryoSift into a fully automated processing pipeline using the existing cryosparc-tools library. Our integrated and customizable 2D assessment workflow enables high-throughput processing that accommodates experienced to novice cryo-EM users.
Authors
- Jan-Hannes Schäfer (ORCID: https://orcid.org/0000-0001-8163-1629)
- Ruizhi Peng (ORCID: https://orcid.org/0000-0001-6057-8961)
- Gabriel C. Lander (ORCID: https://orcid.org/0000-0003-4921-1135)
- Michael A. Cianfrocco (ORCID: https://orcid.org/0000-0002-2067-4999)
- Scott M. Stagg (ORCID: https://orcid.org/0000-0001-8855-4152)
- Nebojša Bogdanović (ORCID: https://orcid.org/0000-0002-6811-2848)
- Puneeth Damodar (ORCID: https://orcid.org/0009-0000-3250-2337)
- Austin Calza (ORCID: https://orcid.org/0009-0007-6348-3985)
- Keegan Hom (ORCID: https://orcid.org/0009-0003-2839-0602)
Institutions
- Florida State University (US)
- Scripps Institution of Oceanography (US)
- University of Michigan (US)
Publication Details
- Journal
- Acta Crystallographica Section F Structural Biology Communications
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1107/s2053230x25008866
- Primary Topic
- Advanced Electron Microscopy Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00