Interpretation of allele-specific chromatin accessibility using cell state-aware 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 a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanoc

SUBMITTER: Atak ZK 

PROVIDER: S-EPMC8168584 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature