Predicting the impact of non-coding variants on DNA methylation
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Predicting the impact.pdf
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1008.21 KB
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Author(s)
Zeng, Haoyang
Gifford, David K
Date Issued
March 2017
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Zeng, Haoyang, and David K. Gifford. “Predicting the Impact of Non-Coding Variants on DNA Methylation.” Nucleic Acids Research 45, no. 11 (March 16, 2017): e99–e99.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial 4.0 International
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DOI of Published Version
https://doi.org/10.1093/nar/gkx177
