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

ABSTRACT: 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.

SUBMITTER: Hutton ER

PROVIDER: S-EPMC8459512 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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

Hutton Elizabeth R ER Vakoc Christopher R CR Siepel Adam A

Genome biology 20210923 1

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 know ...[more]

PMID: 34556174

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