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A method for benchmarking genetic screens reveals a predominant mitochondrial bias

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of...

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Autores principales: Rahman, Mahfuzur, Billmann, Maximilian, Costanzo, Michael, Aregger, Michael, Tong, Amy H Y, Chan, Katherine, Ward, Henry N, Brown, Kevin R, Andrews, Brenda J, Boone, Charles, Moffat, Jason, Myers, Chad L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138267/
https://www.ncbi.nlm.nih.gov/pubmed/34018332
http://dx.doi.org/10.15252/msb.202010013
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author Rahman, Mahfuzur
Billmann, Maximilian
Costanzo, Michael
Aregger, Michael
Tong, Amy H Y
Chan, Katherine
Ward, Henry N
Brown, Kevin R
Andrews, Brenda J
Boone, Charles
Moffat, Jason
Myers, Chad L
author_facet Rahman, Mahfuzur
Billmann, Maximilian
Costanzo, Michael
Aregger, Michael
Tong, Amy H Y
Chan, Katherine
Ward, Henry N
Brown, Kevin R
Andrews, Brenda J
Boone, Charles
Moffat, Jason
Myers, Chad L
author_sort Rahman, Mahfuzur
collection PubMed
description We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.
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spelling pubmed-81382672021-06-03 A method for benchmarking genetic screens reveals a predominant mitochondrial bias Rahman, Mahfuzur Billmann, Maximilian Costanzo, Michael Aregger, Michael Tong, Amy H Y Chan, Katherine Ward, Henry N Brown, Kevin R Andrews, Brenda J Boone, Charles Moffat, Jason Myers, Chad L Mol Syst Biol Reports We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them. John Wiley and Sons Inc. 2021-05-20 /pmc/articles/PMC8138267/ /pubmed/34018332 http://dx.doi.org/10.15252/msb.202010013 Text en © 2021 The Authors. Published under the terms of the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Reports
Rahman, Mahfuzur
Billmann, Maximilian
Costanzo, Michael
Aregger, Michael
Tong, Amy H Y
Chan, Katherine
Ward, Henry N
Brown, Kevin R
Andrews, Brenda J
Boone, Charles
Moffat, Jason
Myers, Chad L
A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title_full A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title_fullStr A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title_full_unstemmed A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title_short A method for benchmarking genetic screens reveals a predominant mitochondrial bias
title_sort method for benchmarking genetic screens reveals a predominant mitochondrial bias
topic Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138267/
https://www.ncbi.nlm.nih.gov/pubmed/34018332
http://dx.doi.org/10.15252/msb.202010013
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