Nonparametric Bayesian clustering to detect bipolar methylated genomic loci

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dc.contributor.authorWu, Xiaoweien
dc.contributor.authorSun, Ming-anen
dc.contributor.authorZhu, Hongxiaoen
dc.contributor.authorXie, Hehuangen
dc.contributor.departmentStatisticsen
dc.date.accessioned2017-02-02T22:54:04Zen
dc.date.available2017-02-02T22:54:04Zen
dc.date.issued2015-01-16en
dc.description.abstractBackground: With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events. Among these methylation patterns, bipolar patterns are important as they may originate from allele-specific methylation (ASM) or cell-specific methylation (CSM). Results: Utilizing nonparametric Bayesian clustering followed by hypothesis testing, we have developed a novel statistical approach to identify bipolar methylated genomic regions in bisulfite sequencing data. Simulation studies demonstrate that the proposed method achieves good performance in terms of specificity and sensitivity. We used the method to analyze data from mouse brain and human blood methylomes. The bipolar methylated segments detected are found highly consistent with the differentially methylated regions identified by using purified cell subsets. Conclusions: Bipolar DNA methylation often indicates epigenetic heterogeneity caused by ASM or CSM. With allele-specific events filtered out or appropriately taken into account, our proposed approach sheds light on the identification of cell-specific genes/pathways under strong epigenetic control in a heterogeneous cell population.en
dc.description.versionPublished versionen
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1186/s12859-014-0439-2en
dc.identifier.issn1471-2105en
dc.identifier.urihttp://hdl.handle.net/10919/74908en
dc.identifier.volume16en
dc.language.isoenen
dc.publisherBiomed Centralen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000348816000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderThe Author(s)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBiochemical Research Methodsen
dc.subjectBiotechnology & Applied Microbiologyen
dc.subjectMathematical & Computational Biologyen
dc.subjectBiochemistry & Molecular Biologyen
dc.subjectDNA methylationen
dc.subjectEpigeneticsen
dc.subjectNonparametric Bayesianen
dc.subjectBISULFITE SEQUENCING DATAen
dc.subjectDNA METHYLATIONen
dc.subjectCAENORHABDITIS-ELEGANSen
dc.subjectEPIGENOMIC ANALYSISen
dc.subjectHUMAN-NEUTROPHILSen
dc.subjectALU REPEATSen
dc.subjectPATTERNSen
dc.subjectORIGINen
dc.subjectDIFFERENTIATIONen
dc.subjectSPECIFICITYen
dc.titleNonparametric Bayesian clustering to detect bipolar methylated genomic locien
dc.title.serialBMC Bioinformaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Statisticsen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Instituteen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/Researchersen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/SelectedFaculty1en

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