CryoSift - An accessible and automated CNN-driven tool for cryo-EM 2D class selection

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 dataset processing. Here, we present a platform-independent convolutional neural network (CNN) tool for assessing the quality of 2D averages to enable 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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/667259v2_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@1fdac98org.highwire.dtl.DTLVardef@14e3c7corg.highwire.dtl.DTLVardef@16520bforg.highwire.dtl.DTLVardef@48fa0f_HPS_FORMAT_FIGEXP M_FIG C_FIG