VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.
BMC Biotechnol
; 19(1): 40, 2019 06 27.
Article
em En
| MEDLINE
| ID: mdl-31248401
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 .Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Marcação de Genes
/
Biologia Computacional
/
Genômica
/
Sistemas CRISPR-Cas
/
Edição de Genes
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
BMC Biotechnol
Assunto da revista:
BIOTECNOLOGIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Austrália