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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

BACKGROUND: Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpi...

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Autores principales: Kim, Eiru, Hart, Traver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789424/
https://www.ncbi.nlm.nih.gov/pubmed/33407829
http://dx.doi.org/10.1186/s13073-020-00809-3
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author Kim, Eiru
Hart, Traver
author_facet Kim, Eiru
Hart, Traver
author_sort Kim, Eiru
collection PubMed
description BACKGROUND: Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. RESULTS: We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. CONCLUSIONS: BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).
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spelling pubmed-77894242021-01-07 Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier Kim, Eiru Hart, Traver Genome Med Software BACKGROUND: Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. RESULTS: We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. CONCLUSIONS: BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel). BioMed Central 2021-01-06 /pmc/articles/PMC7789424/ /pubmed/33407829 http://dx.doi.org/10.1186/s13073-020-00809-3 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Software
Kim, Eiru
Hart, Traver
Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title_full Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title_fullStr Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title_full_unstemmed Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title_short Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier
title_sort improved analysis of crispr fitness screens and reduced off-target effects with the bagel2 gene essentiality classifier
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789424/
https://www.ncbi.nlm.nih.gov/pubmed/33407829
http://dx.doi.org/10.1186/s13073-020-00809-3
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