VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9
BMC biotechnology, 2019•Springer
Background Natural 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). Results VARSCOT 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 …
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). Results VARSCOT 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 …
Background
Natural 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).
Results
VARSCOT 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.
Conclusions
VARSCOT 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 .
Springer
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