Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases

By Richard L. Frock, Jiazhi Hu, R. Meyers, Yu-jui Ho, Erina Kii, F. Alt. Nature Biotechnology, 2014

659 citations recorded by the source corpus.

Although great progress has been made in the characterization of the off-target effects of engineered nucleases, sensitive and unbiased genome-wide methods for the detection of off-target cleavage events and potential collateral damage are still lacking. Here we describe a linear amplification–mediated modification of a previously published high-throughput, genome-wide, translocation sequencing (HTGTS) method that robustly detects DNA double-stranded breaks (DSBs) generated by engineered nucleases across the human genome based on their translocation to other endogenous or ectopic DSBs. HTGTS with different Cas9:sgRNA or TALEN nucleases revealed off-target hotspot numbers for given nucleases that ranged from a few or none to dozens or more, and extended the number of known off-targets for certain previously characterized nucleases more than tenfold. We also identified translocations between bona fide nuclease targets on homologous chromosomes, an undesired collateral effect that has not been described previously. Finally, HTGTS confirmed that the Cas9D10A paired nickase approach suppresses off-target cleavage genome-wide.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cfc05010171666904f71fdf2e12952a80c5baa74

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Citations in the corpus

In vivo genome editing using Staphylococcus aureus Cas9 (2015, background): “However, the off-target sites identified in vitro might differ from those in vivo , which need to be further evaluated by the in vivo applications of BLESS or other unbiased techniques such as those published during the revision of this work 43,44 .”
Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR (2016, background): “The studies are indicated by symbols, explained in the legend of the graph [3, 7, 26, 28, 29, 33, 38].”
Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs (2018, methodology): “To incorporate DNase I peak data into the aggregation model (the only model for which we had training data with corresponding DNase I data), we tried four different approaches: (1) using the DNase I as a ‘mask’ on the values output from the gRNA–target scorer (that is, taking the element-wise produc”
DeepCRISPR: optimized CRISPR guide RNA design by deep learning (2018, methodology): “…by whole sgRNA off-target cleavage detection techniques like GUIDE-seq [22], Digenome-seq [16, 23, 24], High-throughput genome-wide translocation sequencing ( HTGTS ) [25], direct in situ breaks labeling sequencing ( BLESS ) [26], and integration-deficient lentiviral vector capture ( IDLV ) [27].”