CryoSift: 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.