TY  - JOUR
A1  - Atak, Zeynep Kalender
A1  - Taskiran, Ibrahim Ihsan
A1  - Demeulemeester, Jonas
A1  - Flerin, Christopher
A1  - Mauduit, David
A1  - Minnoye, Liesbeth
A1  - Hulselmans, Gert
A1  - Christiaens, Valerie
A1  - Ghanem, Ghanem-Elias
A1  - Wouters, Jasper
A1  - Aerts, Stein
T1  - Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
Y1  - 2021/06/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1082 
EP  - 1096 
DO  - 10.1101/gr.260851.120 
VL  - 31 
IS  - 6 
UR  - http://genome.cshlp.org/content/31/6/1082.abstract 
N2  - 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. 
ER  -