dc.contributor.author | Zeng, Haoyang | |
dc.contributor.author | Gifford, David K | |
dc.date.accessioned | 2017-06-21T16:23:09Z | |
dc.date.available | 2017-06-21T16:23:09Z | |
dc.date.issued | 2017-03 | |
dc.date.submitted | 2017-02 | |
dc.identifier.issn | 0305-1048 | |
dc.identifier.issn | 1362-4962 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/110127 | |
dc.description.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. | en_US |
dc.description.sponsorship | National Cancer Institute (U.S.) (R01HG008363) | en_US |
dc.description.sponsorship | National Cancer Institute (U.S.) (U01HG007037) | en_US |
dc.description.sponsorship | NVIDIA Corporation | en_US |
dc.language.iso | en_US | |
dc.publisher | Oxford University Press | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1093/nar/gkx177 | en_US |
dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en_US |
dc.source | Oxford University Press | en_US |
dc.title | Predicting the impact of non-coding variants on DNA methylation | en_US |
dc.type | Article | en_US |
dc.identifier.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. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.mitauthor | Zeng, Haoyang | |
dc.contributor.mitauthor | Gifford, David K | |
dc.relation.journal | Nucleic Acids Research | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.orderedauthors | Zeng, Haoyang; Gifford, David K. | en_US |
dspace.embargo.terms | N | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-1057-2865 | |
dc.identifier.orcid | https://orcid.org/0000-0003-1709-4034 | |
mit.license | PUBLISHER_CC | en_US |
mit.metadata.status | Complete | |