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Title | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. |
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Journal, issue, pages | Nat Methods, Vol. 16, Issue 11, Page 1153-1160, Year 2019 |
Publish date | Oct 7, 2019 |
Authors | Tristan Bepler / Andrew Morin / Micah Rapp / Julia Brasch / Lawrence Shapiro / Alex J Noble / Bonnie Berger / |
PubMed 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 ...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 ). |
External links | Nat Methods / PubMed:31591578 / PubMed Central |
Methods | EM (single particle) |
Resolution | 3.7 - 3.92 Å |
Structure data | EMDB-20529: EMDB-20531: EMDB-20532: |
Source |
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