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Predicting expression: the complementary power of histone modification and transcription factor binding data
BACKGROUND: Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258808/ https://www.ncbi.nlm.nih.gov/pubmed/25489339 http://dx.doi.org/10.1186/1756-8935-7-36 |
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author | Budden, David M Hurley, Daniel G Cursons, Joseph Markham, John F Davis, Melissa J Crampin, Edmund J |
author_facet | Budden, David M Hurley, Daniel G Cursons, Joseph Markham, John F Davis, Melissa J Crampin, Edmund J |
author_sort | Budden, David M |
collection | PubMed |
description | BACKGROUND: Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the interactions of DNA, TFs and HMs. These models have yielded an unexpected and poorly understood result: that TFs and HMs are statistically redundant in explaining mRNA transcript abundance at a genome-wide level. RESULTS: We constructed predictive models of gene expression by integrating RNA-sequencing, TF and HM chromatin immunoprecipitation sequencing and DNase I hypersensitivity data for two mammalian cell types. All models identified genome-wide statistical redundancy both within and between TFs and HMs, as previously reported. To investigate potential explanations, groups of genes were constructed for ontology-classified biological processes. Predictive models were constructed for each process to explore the distribution of statistical redundancy. We found significant variation in the predictive capacity of TFs and HMs across these processes and demonstrated the predictive power of HMs to be inversely proportional to process enrichment for housekeeping genes. CONCLUSIONS: It is well established that the roles played by TFs and HMs are not functionally redundant. Instead, we attribute the statistical redundancy reported in this and previous genome-wide modelling studies to the heterogeneous distribution of HMs across chromatin domains. Furthermore, we conclude that statistical redundancy between individual TFs can be readily explained by nucleosome-mediated cooperative binding. This could possibly help the cell confer regulatory robustness by rejecting signalling noise and allowing control via multiple pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-7-36) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4258808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42588082014-12-09 Predicting expression: the complementary power of histone modification and transcription factor binding data Budden, David M Hurley, Daniel G Cursons, Joseph Markham, John F Davis, Melissa J Crampin, Edmund J Epigenetics Chromatin Research BACKGROUND: Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the interactions of DNA, TFs and HMs. These models have yielded an unexpected and poorly understood result: that TFs and HMs are statistically redundant in explaining mRNA transcript abundance at a genome-wide level. RESULTS: We constructed predictive models of gene expression by integrating RNA-sequencing, TF and HM chromatin immunoprecipitation sequencing and DNase I hypersensitivity data for two mammalian cell types. All models identified genome-wide statistical redundancy both within and between TFs and HMs, as previously reported. To investigate potential explanations, groups of genes were constructed for ontology-classified biological processes. Predictive models were constructed for each process to explore the distribution of statistical redundancy. We found significant variation in the predictive capacity of TFs and HMs across these processes and demonstrated the predictive power of HMs to be inversely proportional to process enrichment for housekeeping genes. CONCLUSIONS: It is well established that the roles played by TFs and HMs are not functionally redundant. Instead, we attribute the statistical redundancy reported in this and previous genome-wide modelling studies to the heterogeneous distribution of HMs across chromatin domains. Furthermore, we conclude that statistical redundancy between individual TFs can be readily explained by nucleosome-mediated cooperative binding. This could possibly help the cell confer regulatory robustness by rejecting signalling noise and allowing control via multiple pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1756-8935-7-36) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-24 /pmc/articles/PMC4258808/ /pubmed/25489339 http://dx.doi.org/10.1186/1756-8935-7-36 Text en © Budden et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Budden, David M Hurley, Daniel G Cursons, Joseph Markham, John F Davis, Melissa J Crampin, Edmund J Predicting expression: the complementary power of histone modification and transcription factor binding data |
title | Predicting expression: the complementary power of histone modification and transcription factor binding data |
title_full | Predicting expression: the complementary power of histone modification and transcription factor binding data |
title_fullStr | Predicting expression: the complementary power of histone modification and transcription factor binding data |
title_full_unstemmed | Predicting expression: the complementary power of histone modification and transcription factor binding data |
title_short | Predicting expression: the complementary power of histone modification and transcription factor binding data |
title_sort | predicting expression: the complementary power of histone modification and transcription factor binding data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258808/ https://www.ncbi.nlm.nih.gov/pubmed/25489339 http://dx.doi.org/10.1186/1756-8935-7-36 |
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