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dc.contributor.authorChen, Rujian
dc.contributor.authorGifford, David K
dc.date.accessioned2018-01-22T16:19:32Z
dc.date.available2018-01-22T16:19:32Z
dc.date.issued2017-07
dc.date.submitted2016-09
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/113255
dc.description.abstractThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. We characterize how genomic variants that alter chromatin accessibility influence regulatory factor binding with a new method called DeltaBind that predicts condition specific factor binding more accurately than other methods based on DNase-seq data. Using DeltaBind and DNase-seq experiments we predicted the differential binding of 18 factors in K562 and GM12878 cells with an average precision of 28% at 10% recall, with the prediction of individual factors ranging from 5% to 65% precision. We further found that genome variants that alter chromatin accessibility are not necessarily predictive of altering proximal factor binding. Taken together these findings suggest that DNase-seq or ATAC-seq Quantitative Trait Loci (dsQTLs), while important, must be considered in a broader context to establish causality for phenotypic changes.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant U01HG007037)en_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/JOURNAL.PONE.0179411en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_US
dc.sourcePLoSen_US
dc.titleDifferential chromatin profiles partially determine transcription factor bindingen_US
dc.typeArticleen_US
dc.identifier.citationChen, Rujian, and Gifford, David K. “Differential Chromatin Profiles Partially Determine Transcription Factor Binding.” Edited by Roberto Mantovani. PLOS ONE 12, 7 (July 2017): e0179411 © 2017 Chen and Gifforden_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorChen, Rujian
dc.contributor.mitauthorGifford, David K
dc.relation.journalPLOS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-01-19T16:28:49Z
dspace.orderedauthorsChen, Rujian; Gifford, David K.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3861-1758
dc.identifier.orcidhttps://orcid.org/0000-0003-1709-4034
mit.licensePUBLISHER_CCen_US


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