Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of...

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Autores principales:	Chieh-Chun Chen, Shu Xiao, Dan Xie, Xiaoyi Cao, Chun-Xiao Song, Ting Wang, Chuan He, Sheng Zhong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5b3661a5dee74a30bcff9e21e533e1d2
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spelling	oai:doaj.org-article:5b3661a5dee74a30bcff9e21e533e1d22021-11-18T05:53:19ZUnderstanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.1553-734X1553-735810.1371/journal.pcbi.1003367https://doaj.org/article/5b3661a5dee74a30bcff9e21e533e1d22013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24339764/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).Chieh-Chun ChenShu XiaoDan XieXiaoyi CaoChun-Xiao SongTing WangChuan HeSheng ZhongPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 12, p e1003367 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Chieh-Chun Chen Shu Xiao Dan Xie Xiaoyi Cao Chun-Xiao Song Ting Wang Chuan He Sheng Zhong Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
description	Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).
format	article
author	Chieh-Chun Chen Shu Xiao Dan Xie Xiaoyi Cao Chun-Xiao Song Ting Wang Chuan He Sheng Zhong
author_facet	Chieh-Chun Chen Shu Xiao Dan Xie Xiaoyi Cao Chun-Xiao Song Ting Wang Chuan He Sheng Zhong
author_sort	Chieh-Chun Chen
title	Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
title_short	Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
title_full	Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
title_fullStr	Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
title_full_unstemmed	Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
title_sort	understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/5b3661a5dee74a30bcff9e21e533e1d2
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Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.

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