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Budden DM, Hurley DG, Cursons J, et al. Predicting expression: the complementary power of histone modification and transcription factor binding data. Epigenetics Chromatin. 2014;7(1):36doi: 10.1186/1756-8935-7-36.
Budden, D. M., Hurley, D. G., Cursons, J., Markham, J. F., Davis, M. J., & Crampin, E. J. (2014). Predicting expression: the complementary power of histone modification and transcription factor binding data. Epigenetics & chromatin, 7(1), 36. https://doi.org/10.1186/1756-8935-7-36
Budden, David M, et al. "Predicting expression: the complementary power of histone modification and transcription factor binding data." Epigenetics & chromatin vol. 7,1 (2014): 36. doi: https://doi.org/10.1186/1756-8935-7-36
Budden DM, Hurley DG, Cursons J, Markham JF, Davis MJ, Crampin EJ. Predicting expression: the complementary power of histone modification and transcription factor binding data. Epigenetics Chromatin. 2014 Nov 24;7(1):36. doi: 10.1186/1756-8935-7-36. eCollection 2014. PMID: 25489339; PMCID: PMC4258808.
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Epigenetics Chromatin. 2014 Nov 24;7(1):36. doi: 10.1186/1756-8935-7-36. eCollection 2014.

Predicting expression: the complementary power of histone modification and transcription factor binding data.

Epigenetics & chromatin

David M Budden, Daniel G Hurley, Joseph Cursons, John F Markham, Melissa J Davis, Edmund J Crampin

Affiliations

  1. Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, 3010 Parkville, Australia ; NICTA Victoria Research Laboratory, The University of Melbourne, 3010 Parkville, Australia.
  2. Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, 3010 Parkville, Australia.
  3. Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, 3010 Parkville, Australia ; The Walter and Eliza Hall Institute of Medical Research, Department of Medical Biology, The University of Melbourne, 3010 Parkville, Australia.
  4. Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, 3010 Parkville, Australia ; NICTA Victoria Research Laboratory, The University of Melbourne, 3010 Parkville, Australia ; The Walter and Eliza Hall Institute of Medical Research, Department of Medical Biology, The University of Melbourne, 3010 Parkville, Australia ; ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, 3010 Parkville, Australia ; Department of Mathematics and Statistics, The University of Melbourne, 3010 Parkville, Australia ; School of Medicine, The University of Melbourne, 3010 Parkville, Australia.

PMID: 25489339 PMCID: PMC4258808 DOI: 10.1186/1756-8935-7-36

Abstract

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.

Keywords: Gene expression; Histone modifications; Predictive modelling; Transcription factors; Transcriptional regulation

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