An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens.
CRISPR-Cas9 screens
bias correction
cancer dependency
copy number
data exploration
data visualization
gene essentiality
post-genomic data
unsupervised analysis
web application
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
23 01 2023
23 01 2023
Historique:
received:
07
04
2022
revised:
06
10
2022
accepted:
07
12
2022
entrez:
23
2
2023
pubmed:
24
2
2023
medline:
24
2
2023
Statut:
epublish
Résumé
A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR
Identifiants
pubmed: 36814834
doi: 10.1016/j.crmeth.2022.100373
pii: S2667-2375(22)00275-2
pmc: PMC9939378
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100373Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
F.I. receives funding from Open Targets, a public-private initiative involving academia and industry, and performs consultancy for the joint CRUK-AstraZeneca Functional Genomics Center.
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