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Tools for experimental and computational analyses of off-target editing by programmable nucleases

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.

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Fig. 1: Schematics showing four major classes of programmable nucleases.
Fig. 2: Schematics showing in vitro and in vivo experimental techniques most commonly used to characterize off-target cutting by CRISPR–Cas9.
Fig. 3: The ability of various off-target site identification algorithms to correctly rank experimentally confirmed true off-target sites.
Fig. 4: Recommended workflow for identifying CRISPR–Cas9 off-target editing.

Data availability

The data sources are available at https://github.com/baolab-rice/OT-review. Supplementary Tables 3, 4 and 5 contain the data used in the computational techniques performance assessment.

Code availability

The processing scripts are available at https://github.com/baolab-rice/OT-review.

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Acknowledgements

This work was supported by the National Institutes of Health (UG3HL151545, R01HL152314 and OT2HL154977 to G.B.) and the Cancer Prevention and Research Institute of Texas (RR140081 to G.B.).

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Contributions

G.B. and X.R.B. designed the study. X.R.B. and C.M.L. performed the evaluation of experimental methods. Y.P. and X.R.B. performed the evaluation of computational methods. All authors contributed to the discussion and writing of the manuscript.

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Correspondence to Gang Bao.

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Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Supplementary Table 1

Structures and training sets of machine learning-based algorithms for off-target prediction.

Supplementary Table 2

List of datasets from the Sequence Read Archive used to calculate on-target enrichment for performance comparison of experimental techniques. All genomic coordinates reference the hg38 human genome assembly.

Supplementary Table 3

Components of the true-positive list used in the analysis for performance comparison of computational techniques, taken from nine studies that used amplicon-specific experimental techniques to detect off-target editing rates. ‘OT’ in this table stands for off-target.

Supplementary Table 4

The full off-target dataset used in the performance assessment of computational techniques. Potential off-target sites of 27 gRNAs from nine studies shown in Supplementary Table 3 were screened by Cas-OFFinder, allowing up to four mismatches and one base DNA/RNA bulge and scored by each of the algorithms. ‘noind’: DNA/RNA sequences before alignments. Note that, after introducing bulges, one locus might be called by Cas-OFFinder multiple times with different alignment patterns, leading to different scores. These sites were treated the same as other off-target sites without manipulation. Source data for Supplementary Figure 1 and Fig. 3a.

Supplementary Table 5

The off-target dataset of novel gRNAs used in the performance assessment of computational techniques. Potential off-target sites and scores of four gRNAs that were not in the CRISPOR dataset1. These were screened by Cas-OFFinder, allowing up to four mismatches and one base DNA/RNA bulge and scored by each of the algorithms. Note that, after introducing bulges, one locus might be called by Cas-OFFinder multiple times with different alignment patterns, leading to different scores. These sites were treated the same as other off-target sites without manipulation. ‘noind’: DNA/RNA sequences before alignments. Source data for Supplementary Fig. 2 and Fig. 3b.

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Bao, X.R., Pan, Y., Lee, C.M. et al. Tools for experimental and computational analyses of off-target editing by programmable nucleases. Nat Protoc 16, 10–26 (2021). https://doi.org/10.1038/s41596-020-00431-y

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