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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...

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Detalles Bibliográficos
Autores principales: Hart, Traver, Moffat, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
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.
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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
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