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

نویسندگان

  • Feng Wang
  • Huichao Gong
  • Gaochao liu
  • Meijing Li
  • Chuangye Yan
  • Tian Xia
  • Xueming Li
  • Jianyang Zeng
چکیده

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python.

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عنوان ژورنال:
  • Journal of structural biology

دوره 195 3  شماره 

صفحات  -

تاریخ انتشار 2016