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Particle picking is a time-consuming step in single-particle analysis andoften requires significant interventions from users, which has become abottleneck for future automated electron cryo-microscopy (cryo-EM). Here wereport a deep learning framework, called DeepPicker, to address this problemand fill the current gaps toward a fully automated cryo-EM pipeline. DeepPickeremploys a novel cross-molecule training strategy to capture common features ofparticles from previously-analyzed micrographs, and thus does not require anyhuman intervention during particle picking. Tests on the recently-publishedcryo-EM data of three complexes have demonstrated that our deep learning basedscheme can successfully accomplish the human-level particle picking process andidentify a sufficient number of particles that are comparable to those manuallyby human experts. These results indicate that DeepPicker can provide apractically useful tool to significantly reduce the time and manual effortspent in single-particle analysis and thus greatly facilitate high-resolutioncryo-EM structure determination.

DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM