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Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Open accessAbstract
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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Wang, N., Xing, J., Su, X., Pan, J., Chen, H., Shi, L., Si, L., Yang, W., & Li, M. (2024) · Molecular Plant · doi:10.1016/j.molp.2024.09.010 · Cited by 5
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Metadata
- Authors
- Bepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A. J., & Berger, B.
- Venue
- Nature Methods
- Published
- 2019-10-07
- Volume
- 16
- Issue
- 11
- Pages
- 1153-1160
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2793402357
- Cited by
- 1,606
- References
- 2
- FWCI
- 31.95
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
