Predicting the impact of non-coding variants on DNA methylation.
DNA methylation plays a crucial role in the establishment of tissue-specific gene expression and the regulation of key biological processes. However, our present inability to predict the effect of genome sequence variation on DNA methylation precludes a comprehensive assessment of the consequences of non-coding variation. We introduce CpGenie, a sequence-based framework that learns a regulatory code of DNA methylation using a deep convolutional neural network and uses this network to predict the impact of sequence variation on proximal CpG site DNA methylation. CpGenie produces allele-specific DNA methylation prediction with single-nucleotide sensitivity that enables accurate prediction of methylation quantitative trait loci (meQTL). We demonstrate that CpGenie prioritizes validated GWAS SNPs, and contributes to the prediction of functional non-coding variants, including expression quantitative trait loci (eQTL) and disease-associated mutations. CpGenie is publicly available to assist in identifying and interpreting regulatory non-coding variants.
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology Cambridge, MA 02142, USA.
Haoyang
H
Zeng
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology Cambridge, MA 02142, USA.
David K
DK
Gifford
D001483:Base Sequence; D001665:Binding Sites; D016384:Consensus Sequence; D019175:DNA Methylation; D021901:DNA, Intergenic / Q000235:genetics*; D044127:Epigenesis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D008957:Models, Genetic; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D017422:Sequence Analysis, DNA / Q000379:methods*
D021901:DNA, Intergenic