Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions

Peng, H; Zheng, Y; Zhao, Z; Liu, T; Li, J

Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions

Peng, H

Zheng, Y Zhao, Z

Liu, T Li, J

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Publication Type:: Journal Article
Citation:: Bioinformatics, 2018, 34 (17), pp. i757 - i765
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Peng, H https://orcid.org/0000-0002-4379-8097	en_US
dc.contributor.author	Zheng, Y	en_US
dc.contributor.author	Zhao, Z https://orcid.org/0000-0001-5544-4504	en_US
dc.contributor.author	Liu, T	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Bioinformatics, 2018, 34 (17), pp. i757 - i765	en_US
dc.identifier.issn	1367-4803	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132000
dc.description.abstract	© The Author(s) 2018. Published by Oxford University Press. Motivation CRISPR/Cas9 is driving a broad range of innovative applications from basic biology to biotechnology and medicine. One of its current issues is the effect of off-target editing that should be critically resolved and should be completely avoided in the ideal use of this system. Results We developed an ensemble learning method to detect the off-target sites of a single guide RNA (sgRNA) from its thousands of genome-wide candidates. Nucleotide mismatches between on-target and off-target sites have been studied recently. We confirm that there exists strong mismatch enrichment and preferences at the 5′-end close regions of the off-target sequences. Comparing with the on-target sites, sequences of no-editing sites can be also characterized by GC composition changes and position-specific mismatch binary features. Under this novel space of features, an ensemble strategy was applied to train a prediction model. The model achieved a mean score 0.99 of Aera Under Receiver Operating Characteristic curve and a mean score 0.45 of Aera Under Precision-Recall curve in cross-validations on big datasets, outperforming state-of-the-art methods in various test scenarios. Our predicted off-target sites also correspond very well to those detected by high-throughput sequencing techniques. Especially, two case studies for selecting sgRNAs to cure hearing loss and retinal degeneration partly prove the effectiveness of our method. Availability and implementation The python and matlab version of source codes for detecting off-target sites of a given sgRNA and the supplementary files are freely available on the web at https://github.com/penn-hui/OfftargetPredict. Supplementary information Supplementary data are available at Bioinformatics online.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100120
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation.ispartof	Bioinformatics	en_US
dc.relation.isbasedon	10.1093/bioinformatics/bty558	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	RNA, Guide	en_US
dc.subject.mesh	Base Composition	en_US
dc.subject.mesh	Genome	en_US
dc.subject.mesh	Software	en_US
dc.subject.mesh	High-Throughput Nucleotide Sequencing	en_US
dc.subject.mesh	CRISPR-Cas Systems	en_US
dc.subject.mesh	Machine Learning	en_US
dc.title	Recognition of CRISPR/Cas9 off-target sites through ensemble learning of uneven mismatch distributions	en_US
dc.type	Journal Article
utslib.citation.volume	17	en_US
utslib.citation.volume	34	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0604 Genetics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	17	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© The Author(s) 2018. Published by Oxford University Press. Motivation CRISPR/Cas9 is driving a broad range of innovative applications from basic biology to biotechnology and medicine. One of its current issues is the effect of off-target editing that should be critically resolved and should be completely avoided in the ideal use of this system. Results We developed an ensemble learning method to detect the off-target sites of a single guide RNA (sgRNA) from its thousands of genome-wide candidates. Nucleotide mismatches between on-target and off-target sites have been studied recently. We confirm that there exists strong mismatch enrichment and preferences at the 5′-end close regions of the off-target sequences. Comparing with the on-target sites, sequences of no-editing sites can be also characterized by GC composition changes and position-specific mismatch binary features. Under this novel space of features, an ensemble strategy was applied to train a prediction model. The model achieved a mean score 0.99 of Aera Under Receiver Operating Characteristic curve and a mean score 0.45 of Aera Under Precision-Recall curve in cross-validations on big datasets, outperforming state-of-the-art methods in various test scenarios. Our predicted off-target sites also correspond very well to those detected by high-throughput sequencing techniques. Especially, two case studies for selecting sgRNAs to cure hearing loss and retinal degeneration partly prove the effectiveness of our method. Availability and implementation The python and matlab version of source codes for detecting off-target sites of a given sgRNA and the supplementary files are freely available on the web at https://github.com/penn-hui/OfftargetPredict. Supplementary information Supplementary data are available at Bioinformatics online.

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http://hdl.handle.net/10453/132000