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Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, especially for unus...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858545/ https://www.ncbi.nlm.nih.gov/pubmed/31591578 http://dx.doi.org/10.1038/s41592-019-0575-8 |
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author | Bepler, Tristan Morin, Andrew Rapp, Micah Brasch, Julia Shapiro, Lawrence Noble, Alex J. Berger, Bonnie |
author_facet | Bepler, Tristan Morin, Andrew Rapp, Micah Brasch, Julia Shapiro, Lawrence Noble, Alex J. Berger, Bonnie |
author_sort | Bepler, Tristan |
collection | PubMed |
description | Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, 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 (PU) 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 (e.g. small, non-globular, and asymmetric), 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) |
format | Online Article Text |
id | pubmed-6858545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-68585452020-04-07 Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs Bepler, Tristan Morin, Andrew Rapp, Micah Brasch, Julia Shapiro, Lawrence Noble, Alex J. Berger, Bonnie Nat Methods Article Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, 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 (PU) 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 (e.g. small, non-globular, and asymmetric), 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) 2019-10-07 2019-11 /pmc/articles/PMC6858545/ /pubmed/31591578 http://dx.doi.org/10.1038/s41592-019-0575-8 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Bepler, Tristan Morin, Andrew Rapp, Micah Brasch, Julia Shapiro, Lawrence Noble, Alex J. Berger, Bonnie Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title_full | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title_fullStr | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title_full_unstemmed | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title_short | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
title_sort | positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858545/ https://www.ncbi.nlm.nih.gov/pubmed/31591578 http://dx.doi.org/10.1038/s41592-019-0575-8 |
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