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bioRxiv · 10.1101/073809

Discovering DNA motifs and genomic variants associated with DNA methylation

Haoyang Zeng·David K. Gifford
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Abstract

DNA methylation plays a crucial role in establishing tissue-specific gene expression. However, our incomplete understanding of the cis elements that regulate DNA methylation prevents us from interpreting the functional effects of non-coding variants. We present CpGenie (http://cpgenie.csail.mit.edu), a deep convolutional neural network that learns a regulatory sequence code of DNA methylation and enables allele-specific DNA methylation prediction with single-nucleotide sensitivity. Variant annotations from CpGenie accurately identify methylation quantitative trait loci (meQTL) and contribute to the prioritization of functional non-coding variants including expression quantitative trait loci (eQTL) and disease-associated mutations.

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Haoyang Zeng, David K. Gifford. 2016-09-06. Discovering DNA motifs and genomic variants associated with DNA methylation. https://doi.org/10.1101/073809

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