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BAGEL: a computational framework for identifying essential genes from pooled library screens
BACKGROUND: The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. RESULTS: We offer BAGEL (Baye...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833918/ https://www.ncbi.nlm.nih.gov/pubmed/27083490 http://dx.doi.org/10.1186/s12859-016-1015-8 |
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author | Hart, Traver Moffat, Jason |
author_facet | Hart, Traver Moffat, Jason |
author_sort | Hart, Traver |
collection | PubMed |
description | BACKGROUND: The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. RESULTS: We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude. CONCLUSIONS: Using BAGEL, we identify ~2000 fitness genes in pooled library knockout screens in human cell lines at 5 % FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens performed by different labs using different libraries and reagents. |
format | Online Article Text |
id | pubmed-4833918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48339182016-04-17 BAGEL: a computational framework for identifying essential genes from pooled library screens Hart, Traver Moffat, Jason BMC Bioinformatics Software BACKGROUND: The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. RESULTS: We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude. CONCLUSIONS: Using BAGEL, we identify ~2000 fitness genes in pooled library knockout screens in human cell lines at 5 % FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens performed by different labs using different libraries and reagents. BioMed Central 2016-04-16 /pmc/articles/PMC4833918/ /pubmed/27083490 http://dx.doi.org/10.1186/s12859-016-1015-8 Text en © Hart and Moffat. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Hart, Traver Moffat, Jason BAGEL: a computational framework for identifying essential genes from pooled library screens |
title | BAGEL: a computational framework for identifying essential genes from pooled library screens |
title_full | BAGEL: a computational framework for identifying essential genes from pooled library screens |
title_fullStr | BAGEL: a computational framework for identifying essential genes from pooled library screens |
title_full_unstemmed | BAGEL: a computational framework for identifying essential genes from pooled library screens |
title_short | BAGEL: a computational framework for identifying essential genes from pooled library screens |
title_sort | bagel: a computational framework for identifying essential genes from pooled library screens |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833918/ https://www.ncbi.nlm.nih.gov/pubmed/27083490 http://dx.doi.org/10.1186/s12859-016-1015-8 |
work_keys_str_mv | AT harttraver bagelacomputationalframeworkforidentifyingessentialgenesfrompooledlibraryscreens AT moffatjason bagelacomputationalframeworkforidentifyingessentialgenesfrompooledlibraryscreens |