A self-supervised workflow for particle picking in cryo-EM.
IUCrJ
; 7(Pt 4): 719-727, 2020 Jul 01.
Article
em En
| MEDLINE
| ID: mdl-32695418
ABSTRACT
High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0â
Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.
Texto completo:
1
Bases de dados:
MEDLINE
Idioma:
En
Revista:
IUCrJ
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos