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• W2938729367 abstract "DNA 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. We applied this tool on methylome datasets generated for mouse brains which 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." @default.
• W2938729367 created "2019-04-25" @default.
• W2938729367 creator A5014981494 @default.
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• W2938729367 date "2019-01-01" @default.
• W2938729367 modified "2023-10-16" @default.
• W2938729367 title "Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules" @default.
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• W2938729367 doi "https://doi.org/10.1016/j.csbj.2019.04.003" @default.
• W2938729367 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6462766" @default.
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• W2938729367 hasPublicationYear "2019" @default.
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