TY - JOUR T1 - Discovering DNA motifs and genomic variants associated with DNA methylation JF - bioRxiv DO - 10.1101/073809 SP - 073809 AU - Haoyang Zeng AU - David K. Gifford Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/09/06/073809.abstract N2 - 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. ER -