| dc.contributor.author | Sherwood, Richard | |
| dc.contributor.author | Barkal, Amira | |
| dc.contributor.author | Kang, Daniel D | |
| dc.contributor.author | Hashimoto, Tatsunori Benjamin | |
| dc.contributor.author | Engstrom, Logan G. | |
| dc.contributor.author | Gifford, David K | |
| dc.date.accessioned | 2018-01-22T16:10:02Z | |
| dc.date.available | 2018-01-22T16:10:02Z | |
| dc.date.issued | 2017-12 | |
| dc.date.submitted | 2017-03 | |
| dc.identifier.issn | 1932-6203 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/113253 | |
| dc.description.abstract | This 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 describe DNase-capture, an assay that increases the analytical resolution of DNase-seq by focusing its sequencing phase on selected genomic regions. We introduce a new method to compensate for capture bias called BaseNormal that allows for accurate recovery of transcription factor protection profiles from DNase-capture data. We show that these normalized data allow for nuanced detection of transcription factor binding heterogeneity with as few as dozens of sites. | en_US |
| dc.description.sponsorship | National Institute of General Medical Sciences (U.S.) (Grant 1U01HG007037) | en_US |
| dc.publisher | Public Library of Science | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1371/journal.pone.0187046 | en_US |
| dc.rights | Creative Commons Attribution 4.0 International License | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | en_US |
| dc.source | PLoS | en_US |
| dc.title | DNase-capture reveals differential transcription factor binding modalities | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Kang, Daniel et al. “DNase-Capture Reveals Differential Transcription Factor Binding Modalities.” Edited by Deyou Zheng. PLOS ONE 12, 12 (December 2017): e0187046 © 2017 Kang et al | 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 | Kang, Daniel D | |
| dc.contributor.mitauthor | Hashimoto, Tatsunori Benjamin | |
| dc.contributor.mitauthor | Engstrom, Logan G. | |
| dc.contributor.mitauthor | Gifford, David K | |
| dc.relation.journal | PLOS ONE | 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 |
| dc.date.updated | 2018-01-19T16:34:05Z | |
| dspace.orderedauthors | Kang, Daniel; Sherwood, Richard; Barkal, Amira; Hashimoto, Tatsunori; Engstrom, Logan; Gifford, David | en_US |
| dspace.embargo.terms | N | en_US |
| dc.identifier.orcid | https://orcid.org/0000-0003-0521-5855 | |
| dc.identifier.orcid | https://orcid.org/0000-0003-1709-4034 | |
| mit.license | PUBLISHER_CC | en_US |