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

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. Overall design: Accessible chromatin profile of A375 and MM047 lines with ATAC-seq. ChIP-seq against cFos, cJun, Fra-1 and JunB in MM099 line. Please note that processed data file generated from both ATAC-seq replicates is linked to the corresponding *ATAC-seq A sample records.