Discovering functional sequences with RELICS, an analysis method for CRISPR screens

CRISPR screens are a powerful technology for the identification of genome sequences that affect cellular phenotypes such as gene expression, survival, and proliferation. By targeting non-coding sequences for perturbation, CRISPR screens have the potential to systematically discover novel functional...

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Autores principales: Fiaux, Patrick C., Chen, Hsiuyi V., Chen, Poshen B., Chen, Aaron R., McVicker, Graham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521704/
https://www.ncbi.nlm.nih.gov/pubmed/32936799
http://dx.doi.org/10.1371/journal.pcbi.1008194
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author Fiaux, Patrick C.
Chen, Hsiuyi V.
Chen, Poshen B.
Chen, Aaron R.
McVicker, Graham
author_facet Fiaux, Patrick C.
Chen, Hsiuyi V.
Chen, Poshen B.
Chen, Aaron R.
McVicker, Graham
author_sort Fiaux, Patrick C.
collection PubMed
description CRISPR screens are a powerful technology for the identification of genome sequences that affect cellular phenotypes such as gene expression, survival, and proliferation. By targeting non-coding sequences for perturbation, CRISPR screens have the potential to systematically discover novel functional sequences, however, a lack of purpose-built analysis tools limits the effectiveness of this approach. Here we describe RELICS, a Bayesian hierarchical model for the discovery of functional sequences from CRISPR screens. RELICS specifically addresses many of the challenges of non-coding CRISPR screens such as the unknown locations of functional sequences, overdispersion in the observed single guide RNA counts, and the need to combine information across multiple pools in an experiment. RELICS outperforms existing methods with higher precision, higher recall, and finer-resolution predictions on simulated datasets. We apply RELICS to published CRISPR interference and CRISPR activation screens to predict and experimentally validate novel regulatory sequences that are missed by other analysis methods. In summary, RELICS is a powerful new analysis method for CRISPR screens that enables the discovery of functional sequences with unprecedented resolution and accuracy.
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spelling pubmed-75217042020-10-06 Discovering functional sequences with RELICS, an analysis method for CRISPR screens Fiaux, Patrick C. Chen, Hsiuyi V. Chen, Poshen B. Chen, Aaron R. McVicker, Graham PLoS Comput Biol Research Article CRISPR screens are a powerful technology for the identification of genome sequences that affect cellular phenotypes such as gene expression, survival, and proliferation. By targeting non-coding sequences for perturbation, CRISPR screens have the potential to systematically discover novel functional sequences, however, a lack of purpose-built analysis tools limits the effectiveness of this approach. Here we describe RELICS, a Bayesian hierarchical model for the discovery of functional sequences from CRISPR screens. RELICS specifically addresses many of the challenges of non-coding CRISPR screens such as the unknown locations of functional sequences, overdispersion in the observed single guide RNA counts, and the need to combine information across multiple pools in an experiment. RELICS outperforms existing methods with higher precision, higher recall, and finer-resolution predictions on simulated datasets. We apply RELICS to published CRISPR interference and CRISPR activation screens to predict and experimentally validate novel regulatory sequences that are missed by other analysis methods. In summary, RELICS is a powerful new analysis method for CRISPR screens that enables the discovery of functional sequences with unprecedented resolution and accuracy. Public Library of Science 2020-09-16 /pmc/articles/PMC7521704/ /pubmed/32936799 http://dx.doi.org/10.1371/journal.pcbi.1008194 Text en © 2020 Fiaux et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fiaux, Patrick C.
Chen, Hsiuyi V.
Chen, Poshen B.
Chen, Aaron R.
McVicker, Graham
Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title_full Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title_fullStr Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title_full_unstemmed Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title_short Discovering functional sequences with RELICS, an analysis method for CRISPR screens
title_sort discovering functional sequences with relics, an analysis method for crispr screens
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521704/
https://www.ncbi.nlm.nih.gov/pubmed/32936799
http://dx.doi.org/10.1371/journal.pcbi.1008194
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