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

Kalender Atak, Z.; Taskiran, I. I.; Flerin, C.; Mauduit, D.; Minnoye, L.; Hulsemans, G.; Christiaens, V.; Ghanem, G.-E.; Wouters, J.; Aerts, S.

doi:10.1101/2019.12.21.885806

bioRxiv · 10.1101/2019.12.21.885806

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

Kalender Atak, Z.·Taskiran, I. I.·Flerin, C.·Mauduit, D.·Minnoye, L.·Hulsemans, G.·Christiaens, V.·Ghanem, G.-E.·Wouters, J.·Aerts, S.

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Abstract

Brief AbstractPrioritization 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 phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

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Kalender Atak, Z., Taskiran, I. I., Flerin, C., Mauduit, D., Minnoye, L., Hulsemans, G., Christiaens, V., Ghanem, G.-E., Wouters, J., Aerts, S.. 2019-12-23. Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning. https://doi.org/10.1101/2019.12.21.885806

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