Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based ge...

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Autores principales:	Wilson Louie, Max W Shen, Zakir Tahiry, Sophia Zhang, Daniel Worstell, Christopher A Cassa, Richard I Sherwood, David K Gifford
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d7839036eeb942be9da582fbcf3a7ab6
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spelling	oai:doaj.org-article:d7839036eeb942be9da582fbcf3a7ab62021-12-02T19:57:37ZMachine learning based CRISPR gRNA design for therapeutic exon skipping.1553-734X1553-735810.1371/journal.pcbi.1008605https://doaj.org/article/d7839036eeb942be9da582fbcf3a7ab62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008605https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.Wilson LouieMax W ShenZakir TahirySophia ZhangDaniel WorstellChristopher A CassaRichard I SherwoodDavid K GiffordPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 1, p e1008605 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Wilson Louie Max W Shen Zakir Tahiry Sophia Zhang Daniel Worstell Christopher A Cassa Richard I Sherwood David K Gifford Machine learning based CRISPR gRNA design for therapeutic exon skipping.
description	Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.
format	article
author	Wilson Louie Max W Shen Zakir Tahiry Sophia Zhang Daniel Worstell Christopher A Cassa Richard I Sherwood David K Gifford
author_facet	Wilson Louie Max W Shen Zakir Tahiry Sophia Zhang Daniel Worstell Christopher A Cassa Richard I Sherwood David K Gifford
author_sort	Wilson Louie
title	Machine learning based CRISPR gRNA design for therapeutic exon skipping.
title_short	Machine learning based CRISPR gRNA design for therapeutic exon skipping.
title_full	Machine learning based CRISPR gRNA design for therapeutic exon skipping.
title_fullStr	Machine learning based CRISPR gRNA design for therapeutic exon skipping.
title_full_unstemmed	Machine learning based CRISPR gRNA design for therapeutic exon skipping.
title_sort	machine learning based crispr grna design for therapeutic exon skipping.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d7839036eeb942be9da582fbcf3a7ab6
work_keys_str_mv	AT wilsonlouie machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT maxwshen machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT zakirtahiry machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT sophiazhang machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT danielworstell machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT christopheracassa machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT richardisherwood machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping AT davidkgifford machinelearningbasedcrisprgrnadesignfortherapeuticexonskipping
_version_	1718375808546897920

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

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