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
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

100373

Informations 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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Auteurs

Alessandro Vinceti (A)

Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Riccardo Roberto De Lucia (RR)

Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Paolo Cremaschi (P)

Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Umberto Perron (U)

Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Emre Karakoc (E)

Cancer Dependency Map Analytics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Luca Mauri (L)

ICT and Digitalisation, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Carlos Fernandez (C)

ICT and Digitalisation, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Krzysztof Henryk Kluczynski (KH)

ICT and Digitalisation, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Daniel Stephen Anderson (DS)

ICT and Digitalisation, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.

Francesco Iorio (F)

Computational Biology Research Centre, Human Technopole, Viale Rita Levi-Montalcini, 1, 20157 Milano, Italy.
Cancer Dependency Map Analytics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

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Classifications MeSH