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Unsupervised pattern discovery in human chromatin structure through genomic segmentation

dc.contributor.authorHoffman, Michael M.
dc.contributor.authorBuske, Orion J.
dc.contributor.authorWang, Jie
dc.contributor.authorWeng, Zhiping
dc.contributor.authorBilmes, Jeff A.
dc.contributor.authorNoble, William Stafford
dc.date2022-08-11T08:07:59.000
dc.date.accessioned2022-08-23T15:38:01Z
dc.date.available2022-08-23T15:38:01Z
dc.date.issued2012-03-18
dc.date.submitted2013-02-22
dc.identifier.citation<p>Nat Methods. 2012 Mar 18;9(5):473-6. doi: 10.1038/nmeth.1937. <a href="http://dx.doi.org/10.1038/nmeth.1937">Link to article on publisher's site</a></p>
dc.identifier.issn1548-7091 (Linking)
dc.identifier.doi10.1038/nmeth.1937
dc.identifier.pmid22426492
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25847
dc.description.abstractWe trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=22426492&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3340533/
dc.subjectBayes Theorem
dc.subjectChromatin
dc.subject*Genome, Human
dc.subjectHistones
dc.subjectHumans
dc.subjectK562 Cells
dc.subjectMolecular Sequence Data
dc.subjectPromoter Regions, Genetic
dc.subjectRegulatory Sequences, Nucleic Acid
dc.subjectTranscription Factors
dc.subject*Transcription Initiation Site
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectGenetics and Genomics
dc.subjectSystems Biology
dc.titleUnsupervised pattern discovery in human chromatin structure through genomic segmentation
dc.typeJournal Article
dc.source.journaltitleNature methods
dc.source.volume9
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/14
dc.identifier.contextkey3761395
html.description.abstract<p>We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.</p>
dc.identifier.submissionpathbioinformatics_pubs/14
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.contributor.departmentDepartment of Biochemistry and Molecular Pharmacology
dc.source.pages473-6


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