VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9

BMC Biotechnology

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[1] 0000 0000 9116 4836, grid.14095.39, Freie Universität, Berlin, Germany;0000 0000 9071 0620, grid.419538.2, Max Planck Institute for Molecular Genetics, Berlin, Germany;grid.1016.6, CSIRO, Sydney, NSW, Australia;grid.1016.6, CSIRO, Sydney, NSW, Australia;0000 0000 9116 4836, grid.14095.39, Freie Universität, Berlin, Germany;

关键词: CRISPR-Cas9;    Off-target detection;    Variants;    Genome editing;   

DOI  :  10.1186/s12896-019-0535-5

来源: publisher

【 摘 要 】

BackgroundNatural variations in a genome can drastically alter the CRISPR-Cas9 off-target landscape by creating or removing sites. Despite the resulting potential side-effects from such unaccounted for sites, current off-target detection pipelines are not equipped to include variant information. To address this, we developed VARiant-aware detection and SCoring of Off-Targets (VARSCOT).ResultsVARSCOT identifies only 0.6% of off-targets to be common between 4 individual genomes and the reference, with an average of 82% of off-targets unique to an individual. VARSCOT is the most sensitive detection method for off-targets, finding 40 to 70% more experimentally verified off-targets compared to other popular software tools and its machine learning model allows for CRISPR-Cas9 concentration aware off-target activity scoring.ConclusionsVARSCOT allows researchers to take genomic variation into account when designing individual or population-wide targeting strategies. VARSCOT is available from https://github.com/BauerLab/VARSCOT.

【 授权许可】

CC BY