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dc.contributor.authorKellis, Manolis
dc.contributor.authorMortazavi, Ali
dc.contributor.authorPepke, Shirley
dc.contributor.authorJansen, Camden
dc.contributor.authorMarinov, Georgi K.
dc.contributor.authorErnst, Jason
dc.contributor.authorHardison, Ross C.
dc.contributor.authorMyers, Richard M.
dc.contributor.authorWold, Barbara J.
dc.date.accessioned2014-03-17T14:47:28Z
dc.date.available2014-03-17T14:47:28Z
dc.date.issued2013-10
dc.date.submitted2013-03
dc.identifier.issn1088-9051
dc.identifier.urihttp://hdl.handle.net/1721.1/85672
dc.description.abstractWe tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.en_US
dc.language.isoen_US
dc.publisherCold Spring Harbor Laboratory Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1101/gr.158261.113en_US
dc.rightsCreative Commons Attribution-Noncommericalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceCold Spring Harbor Laboratory Pressen_US
dc.titleIntegrating and mining the chromatin landscape of cell-type specificity using self-organizing mapsen_US
dc.typeArticleen_US
dc.identifier.citationMortazavi, A., S. Pepke, C. Jansen, G. K. Marinov, J. Ernst, M. Kellis, R. C. Hardison, R. M. Myers, and B. J. Wold. “Integrating and Mining the Chromatin Landscape of Cell-Type Specificity Using Self-Organizing Maps.” Genome Research 23, no. 12 (December 1, 2013): 2136–2148.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorKellis, Manolisen_US
dc.relation.journalGenome Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsMortazavi, A.; Pepke, S.; Jansen, C.; Marinov, G. K.; Ernst, J.; Kellis, M.; Hardison, R. C.; Myers, R. M.; Wold, B. J.en_US
mit.licensePUBLISHER_CCen_US


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