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Cited by 1,606

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Open access
Bepler, Tristan, Morin, Andrew, Rapp, Micah, Brasch, Julia, Shapiro, Lawrence, Noble, Alex J., Berger, Bonnie·2019·Nature Methods
DOI 10.1038/s41592-019-0575-8
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Abstract

Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source (http://topaz.csail.mit.edu). The challenge of accurate particle picking in cryo-EM analysis is addressed with Topaz, a neural-network-based algorithm that shows advantages over other tools, especially in picking unusually shaped particles.

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Metadata

Authors
Bepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A. J., & Berger, B.
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2019-10-07
Volume
16
Issue
11
Pages
1153-1160
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-019-0575-8
OpenAlex
W2793402357
Cited by
1,606
References
2
FWCI
31.95
Type
journal article
Language
en
Open access
green

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