DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM

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• 作者：Feng Wang^c ; ¹ ; Huichao Gong^b ; ¹ ; Gaochao Liu^a ; ^d ; ¹ ; Meijing Li^a ; ^d ; Chuangye Yan^a ; ^e ; Tian Xia^c ; ^{isutian@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Xueming Li^a ; ^d ; ^e ; ^{lixueming@mail.tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Jianyang Zeng^b ; ^{zengjy321@tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Cryo-EM ; Particle picking ; Automation ; Deep learning
• 刊名：Journal of Structural Biology
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：195
• 期：3
• 页码：325-336
• 全文大小：2900 K

文摘

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.