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Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with motif analysis and deep learning

ABSTRACT: Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or cancer genome, to identify only those variants that impact enhancer function, remains a major challenge. Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to 10 phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

ORGANISM(S): Homo sapiens

PROVIDER: GSE159965 | GEO | 2021/03/03

REPOSITORIES: GEO

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