Tools for experimental and computational analyses of off-target editing by programmable nucleases
dc.contributor.author | Bao, X. Robert | |
dc.contributor.author | Pan, Yidan | |
dc.contributor.author | Lee, Ciaran M. | |
dc.contributor.author | Davis, Timothy H. | |
dc.contributor.author | Bao, Gang | |
dc.contributor.funder | National Institutes of Health | en |
dc.contributor.funder | Cancer Prevention and Research Institute of Texas | en |
dc.date.accessioned | 2021-01-13T09:11:32Z | |
dc.date.available | 2021-01-13T09:11:32Z | |
dc.date.issued | 2020-12-07 | |
dc.date.updated | 2021-01-12T12:47:53Z | |
dc.description.abstract | Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.Off-target effects of programmable nucleases remain a critical issue for therapeutic applications of genome editing. This review compares experimental and computational tools for off-target analysis and provides recommendations for better assessments of off-target effects. | en |
dc.description.sponsorship | National Institutes of Health (UG3HL151545; R01HL152314; OT2HL154977); Cancer Prevention and Research Institute of Texas (RR140081) | en |
dc.description.status | Peer reviewed | en |
dc.description.version | Accepted Version | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Bao, X. R., Pan, Y., Lee, C. M., Davis, T. H. and Bao, G. (2020) 'Tools for experimental and computational analyses of off-target editing by programmable nucleases', Nature Protocols, 16, pp. 10-26. doi: 10.1038/s41596-020-00431-y | en |
dc.identifier.doi | 10.1038/s41596-020-00431-y | en |
dc.identifier.eissn | 1750-2799 | |
dc.identifier.endpage | 26 | en |
dc.identifier.issn | 1754-2189 | |
dc.identifier.journaltitle | Nature Protocols | en |
dc.identifier.startpage | 10 | en |
dc.identifier.uri | https://hdl.handle.net/10468/10901 | |
dc.identifier.volume | 16 | en |
dc.language.iso | en | en |
dc.publisher | Nature Research | en |
dc.rights | © 2020, Springer Nature Limited. This is a post-peer-review, pre-copyedit version of an article published in Nature Protocols on 7 December 2020. The final authenticated version is available online at: https://doi.org/10.1038/s41596-020-00431-y | en |
dc.subject | Double-stranded breaks | en |
dc.subject | Zinc-finger nucleases | en |
dc.subject | Genome-wide analysis | en |
dc.subject | In-vitro | en |
dc.subject | Unbiased detection | en |
dc.subject | High-effeciency | en |
dc.subject | DNA cleavage | en |
dc.subject | Stem-cells | en |
dc.subject | Web tool | en |
dc.subject | CAS9 | en |
dc.title | Tools for experimental and computational analyses of off-target editing by programmable nucleases | en |
dc.type | Article (peer-reviewed) | en |
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