VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.
CRISPR-Cas9
Genome editing
Off-target detection
Variants
Journal
BMC biotechnology
ISSN: 1472-6750
Titre abrégé: BMC Biotechnol
Pays: England
ID NLM: 101088663
Informations de publication
Date de publication:
27 06 2019
27 06 2019
Historique:
received:
06
08
2018
accepted:
17
06
2019
entrez:
29
6
2019
pubmed:
30
6
2019
medline:
14
1
2020
Statut:
epublish
Résumé
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). 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. 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 .
Sections du résumé
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 .
Identifiants
pubmed: 31248401
doi: 10.1186/s12896-019-0535-5
pii: 10.1186/s12896-019-0535-5
pmc: PMC6598273
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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