Journal
GENOME RESEARCH
Volume 31, Issue 6, Pages -Publisher
COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.260851.120
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Funding
- European Research Council Consolidator grant [724226_cisCONTROL]
- KU Leuven [C14/18/092]
- Foundation Against Cancer [2016-070]
- Fonds Wetenschappelijk Onderzoek [1S03317N, 12J6916N]
- Kom op tegen Kanker (Stand up to Cancer)
- Flemish Cancer Society
- Stichting tegen Kanker (Foundation against Cancer)
- Belgian Cancer Society
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This study introduces a deep learning model called DeepMEL2 that outperforms conventional motif-based scoring models in capturing regulatory programs in melanoma cells. It identifies hundreds to thousands of allele-specific chromatin accessibility variants in melanoma genomes, with some attributed to changes in transcription factor binding sites.
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.
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