Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells

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dc.contributor.authorBanerjee, Sharmien
dc.contributor.authorZhu, Hongxiaoen
dc.contributor.authorTang, Manen
dc.contributor.authorFeng, Wu-chunen
dc.contributor.authorWu, Xiaoweien
dc.contributor.authorXie, Hehuang Daviden
dc.contributor.departmentElectrical and Computer Engineeringen
dc.contributor.departmentBiological Sciencesen
dc.contributor.departmentBiomedical Sciences and Pathobiologyen
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentStatisticsen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.contributor.departmentSchool of Neuroscienceen
dc.date.accessioned2019-10-28T13:14:35Zen
dc.date.available2019-10-28T13:14:35Zen
dc.date.issued2019-01-15en
dc.description.abstractGene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.en
dc.description.notesThis work was supported by NIH grant NS094574, the faculty program fund from the Biocomplexity Institute of Virginia Tech to HX, and VT's Open Access Subvention Fund.en
dc.description.sponsorshipNIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [NS094574]; Biocomplexity Institute of Virginia Tech; VT's Open Access Subvention Funden
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fgene.2018.00731en
dc.identifier.issn1664-8021en
dc.identifier.other731en
dc.identifier.pmid30697231en
dc.identifier.urihttp://hdl.handle.net/10919/95183en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjecttranscription factoren
dc.subjectregulatory networken
dc.subjectPoisson processen
dc.subjectchromatin statesen
dc.subjectneural stem cellen
dc.titleIdentifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cellsen
dc.title.serialFrontiers in Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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