ArticleBMC biotechnology2019
VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.
Article in BMC biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Variant-aware Cas-OFFinder: web-based in silico variant-aware potential off-target site identification for genome editing applications.Nucleic acids research · 2025Article
- A bioinformatic analysis of gene editing off-target loci altered by common polymorphisms, using 'PopOff'.Journal of the Royal Society of New Zealand · 2025Article
- 3Bs of CRISPR-Cas mediated genome editing in plants: exploring the basics, bioinformatics and biosafety landscape.Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Review
- Scientific and technological advances in the development of sustainable disease management tools: a case study on kiwifruit bacterial canker.Frontiers in plant science · 2023Review
- A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.Computational and structural biotechnology journal · 2022Review
- Vargas: heuristic-free alignment for assessing linear and graph read aligners.Bioinformatics (Oxford, England) · 2020Article
- New Plant Breeding Techniques in Citrus for the Improvement of Important Agronomic Traits. A Review.Frontiers in plant science · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 .
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Registered trials
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