Repository: Freie Universit├Ąt Berlin, Math Department

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

Wilson, Laurence O. W. and Hetzel, Sara and Pockrandt, Christopher and Reinert, Knut and Bauer, Denis C. (2019) VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9. BMC Biotechnology, 19 (1). ISSN 1472-6750

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Official URL: http://doi.org/10.1186/s12896-019-0535-5

Abstract

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.

Item Type:Article
Uncontrolled Keywords:CRISPR-Cas9, Off-target detection, Variants, Genome editing
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2430
Deposited By: Anja Kasseckert
Deposited On:02 Apr 2020 11:57
Last Modified:02 Apr 2020 11:57

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