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| Title | CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection. |
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| Journal, issue, pages | Acta Crystallogr F Struct Biol Commun, Vol. 81, Issue Pt 12, Page 517-526, Year 2025 |
| Publish date | Dec 1, 2025 |
Authors | Jan Hannes Schäfer / Austin Calza / Keegan Hom / Puneeth Damodar / Ruizhi Peng / Nebojša Bogdanović / Gabriel C Lander / Scott M Stagg / Michael A Cianfrocco / ![]() |
| PubMed Abstract | Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge in cryo-EM data-set processing. ...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. |
External links | Acta Crystallogr F Struct Biol Commun / PubMed:41201048 / PubMed Central |
| Methods | EM (single particle) |
| Resolution | 2.1 Å |
| Structure data | ![]() EMDB-70880: Apoferritin from mouse on graphene |
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