@article{Atak01062021,
author = {Atak, Zeynep Kalender and Taskiran, Ibrahim Ihsan and Demeulemeester, Jonas and Flerin, Christopher and Mauduit, David and Minnoye, Liesbeth and Hulselmans, Gert and Christiaens, Valerie and Ghanem, Ghanem-Elias and Wouters, Jasper and Aerts, Stein}, 
title = {Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning},
volume = {31}, 
number = {6}, 
pages = {1082-1096}, 
year = {2021}, 
doi = {10.1101/gr.260851.120}, 
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 melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%–20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.}, 
URL = {http://genome.cshlp.org/content/31/6/1082.abstract}, 
eprint = {http://genome.cshlp.org/content/31/6/1082.full.pdf+html}, 
journal = {Genome Research} 
}