Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens.

CP: Molecular biology CRISPR-Cas9 screen cancer dependency reference gene scale-limited screen supervised analysis

Journal

Cell reports
ISSN: 2211-1247
Titre abrégé: Cell Rep
Pays: United States
ID NLM: 101573691

Informations de publication

Date de publication:
26 07 2022
Historique:
received: 24 02 2022
revised: 26 05 2022
accepted: 07 07 2022
entrez: 29 7 2022
pubmed: 30 7 2022
medline: 3 8 2022
Statut: ppublish

Résumé

Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens-typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome-constitute an optimal setting for investigating narrow hypotheses and are easier to execute on complex models, such as organoids and in vivo models. Different supervised methods are used for computational analysis of genome-wide CRISPR-Cas9 screens; most are not well suited for scale-limited screens, as they require large sets of positive/negative control genes (gene templates) to be included among the screened ones. Here, we develop a computational framework identifying optimal subsets of known essential and nonessential genes (at different subsampling percentages) that can be used as templates for supervised analyses of scale-limited CRISPR-Cas9 screens, while having a reduced impact on the size of the employed library.

Identifiants

pubmed: 35905712
pii: S2211-1247(22)00954-8
doi: 10.1016/j.celrep.2022.111145
pii:
doi:

Substances chimiques

RNA, Guide 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

111145

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests F.I. receives funds from Open Targets, a public-private initiative involving academia and industry. F.I. performs consultancy for the joint CRUK-AstraZeneca Functional Genomics Centre.

Auteurs

Alessandro Vinceti (A)

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.

Lucia Trastulla (L)

Computational Biology Research Centre, 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. Electronic address: francesco.iorio@fht.org.

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Classifications MeSH