title: DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.
abstract: 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.
authors.affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Wang
authors.affiliation: Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Huichao
authors.initials: H
authors.lastname: Gong
authors.affiliation: School of Life Sciences, Tsinghua University, Beijing 100084, China; Beijing Advanced Innovation Center for Structure Biology, Tsinghua University, Beijing 100084, China.
authors.forename: Gaochao
authors.initials: G
authors.lastname: Liu
authors.affiliation: School of Life Sciences, Tsinghua University, Beijing 100084, China; Beijing Advanced Innovation Center for Structure Biology, Tsinghua University, Beijing 100084, China.
authors.forename: Meijing
authors.initials: M
authors.lastname: Li
authors.affiliation: School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China.
authors.forename: Chuangye
authors.initials: C
authors.lastname: Yan
authors.affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address: isutian@gmail.com.
authors.forename: Tian
authors.initials: T
authors.lastname: Xia
authors.affiliation: School of Life Sciences, Tsinghua University, Beijing 100084, China; Beijing Advanced Innovation Center for Structure Biology, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China. Electronic address: lixueming@mail.tsinghua.edu.cn.
authors.forename: Xueming
authors.initials: X
authors.lastname: Li
authors.affiliation: Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China. Electronic address: zengjy321@tsinghua.edu.cn.
authors.forename: Jianyang
authors.initials: J
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D053829:Amyloid Precursor Protein Secretases / Q000737:chemistry / Q000648:ultrastructure; D020285:Cryoelectron Microscopy / Q000379:methods*; D003627:Data Interpretation, Statistical; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008958:Models, Molecular; D012984:Software; D050916:TRPV Cation Channels / Q000737:chemistry / Q000648:ultrastructure
chemical_list: D050916:TRPV Cation Channels; D053829:Amyloid Precursor Protein Secretases
keywords: Automation; Cryo-EM; Deep learning; Particle picking