Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules

dc.contributor.authorBanerjee, Sharmien
dc.contributor.authorWei, Xiaoranen
dc.contributor.authorXie, Hehuang Daviden
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2019-07-24T17:18:57Zen
dc.date.available2019-07-24T17:18:57Zen
dc.date.issued2019-04-09en
dc.description.abstractDNA methylation is an epigenetic modification modulating the structure of DNA molecule and the interactions with its binding proteins. Accumulating large-scale methylation data motivates the development of analytic tools to facilitate methylome data mining. One critical phenomenon associated with dynamic DNA methylation is the altered DNA binding affinity of transcription factors,which plays key roles in gene expression regulation. In this study,we conceived an algorithm to predict epigenetic regulatory modules through recursive motif analyses on differentially methylated loci. A two-step procedure was implemented to first group differentially methylated loci into clusters according to their correlations in methylation profiles and then to repeatedly identify the transcription factor binding motifs significantly enriched in each cluster. Weapplied this tool on methylome datasets generated for mouse brainswhich have a lack of DNA demethylation enzymes TET1 or TET2. Compared with wild type control, the differentially methylated CpG sites identified in TET1 knockout mouse brains differed significantly from those determined for TET2 knockout. Transcription factors with zinc finger DNA binding domains including Egr1, Zic3, and Zeb1 were predicted to be associated with TET1 mediated brain methylome programming, while Lhx family members with Homeobox domains were predicted to be associated with TET2 function. Interestingly, genomic loci from a co-methylated cluster often host motifs for transcription factors sharing the same DNA binding domains. Altogether, our study provided a systematic approach for epigenetic regulatory module identification and will help throw light on the interplay of DNA methylation and transcription factors.en
dc.description.sponsorshipThis work was supported by NIH grant NS094574 and the Biocomplexity Institute faculty development fund for H.X, and VT's Open Access Subvention Fund.en
dc.format.extent9 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.csbj.2019.04.003en
dc.identifier.urihttp://hdl.handle.net/10919/91951en
dc.identifier.volume17en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDNA methylationen
dc.subjectTranscription factoren
dc.subjectMotif Brain developmenten
dc.subjectRegulatory moduleen
dc.subjectGene expressionen
dc.titleRecursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modulesen
dc.title.serialComputational and Structural Biotechnology Journalen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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