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Title: A self-supervised workflow for particle picking in cryo-EM

Journal Article · · IUCrJ

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 3.0 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.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0012704; LDRD17-023; LDRD19-014
OSTI ID:
1634176
Alternate ID(s):
OSTI ID: 1633026
Report Number(s):
BNL-216002-2020-JAAM; IUCRAJ
Journal Information:
IUCrJ, Vol. 7, Issue 4; ISSN 2052-2525
Publisher:
International Union of CrystallographyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 13 works
Citation information provided by
Web of Science

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Figures / Tables (11)