DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.
J Struct Biol
; 195(3): 325-336, 2016 09.
Article
em En
| MEDLINE
| ID: mdl-27424268
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Microscopia Crioeletrônica
/
Imageamento Tridimensional
Tipo de estudo:
Guideline
Idioma:
En
Revista:
J Struct Biol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
China