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Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequence...

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Autores principales: Smith, Ian, Greenside, Peyton G., Natoli, Ted, Lahr, David L., Wadden, David, Tirosh, Itay, Narayan, Rajiv, Root, David E., Golub, Todd R., Subramanian, Aravind, Doench, John G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5726721/
https://www.ncbi.nlm.nih.gov/pubmed/29190685
http://dx.doi.org/10.1371/journal.pbio.2003213
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author Smith, Ian
Greenside, Peyton G.
Natoli, Ted
Lahr, David L.
Wadden, David
Tirosh, Itay
Narayan, Rajiv
Root, David E.
Golub, Todd R.
Subramanian, Aravind
Doench, John G.
author_facet Smith, Ian
Greenside, Peyton G.
Natoli, Ted
Lahr, David L.
Wadden, David
Tirosh, Itay
Narayan, Rajiv
Root, David E.
Golub, Todd R.
Subramanian, Aravind
Doench, John G.
author_sort Smith, Ian
collection PubMed
description The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function.
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spelling pubmed-57267212017-12-27 Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map Smith, Ian Greenside, Peyton G. Natoli, Ted Lahr, David L. Wadden, David Tirosh, Itay Narayan, Rajiv Root, David E. Golub, Todd R. Subramanian, Aravind Doench, John G. PLoS Biol Methods and Resources The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function. Public Library of Science 2017-11-30 /pmc/articles/PMC5726721/ /pubmed/29190685 http://dx.doi.org/10.1371/journal.pbio.2003213 Text en © 2017 Smith 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 Methods and Resources
Smith, Ian
Greenside, Peyton G.
Natoli, Ted
Lahr, David L.
Wadden, David
Tirosh, Itay
Narayan, Rajiv
Root, David E.
Golub, Todd R.
Subramanian, Aravind
Doench, John G.
Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title_full Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title_fullStr Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title_full_unstemmed Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title_short Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map
title_sort evaluation of rnai and crispr technologies by large-scale gene expression profiling in the connectivity map
topic Methods and Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5726721/
https://www.ncbi.nlm.nih.gov/pubmed/29190685
http://dx.doi.org/10.1371/journal.pbio.2003213
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