ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens.

Hutton, Elizabeth R, Vakoc, Christopher R, Siepel, Adam (September 2021) ACE: a probabilistic model for characterizing gene-level essentiality in CRISPR screens. Genome Biology, 22 (1). p. 278. ISSN 1474-760X

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DOI: 10.1186/s13059-021-02491-z


High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that accounts for multiple sources of variation in CRISPR-Cas9 screens and enables new statistical tests for essentiality. We show using simulations that ACE is effective at predicting both absolute and differential essentiality. When applied to publicly available data, ACE identifies known and novel candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. ACE provides a robust framework for identifying genes responsive to subtype-specific therapeutic targeting.

Item Type: Paper
Subjects: diseases & disorders > cancer
bioinformatics > computational biology
Investigative techniques and equipment > CRISPR-Cas9
CSHL Authors:
Communities: CSHL labs > Siepel lab
CSHL labs > Vakoc lab
School of Biological Sciences > Publications
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 23 September 2021
Date Deposited: 30 Sep 2021 15:56
Last Modified: 31 May 2022 20:54
PMCID: PMC8459512

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