Genome-wide in silico prediction of gene expression

McLeay, Robert C., Lesluyes, Tom, Partida, Gabriel Cuellar and Bailey, Timothy L. (2012) Genome-wide in silico prediction of gene expression. Bioinformatics, 28 21: 2789-2796. doi:10.1093/bioinformatics/bts529

Author McLeay, Robert C.
Lesluyes, Tom
Partida, Gabriel Cuellar
Bailey, Timothy L.
Title Genome-wide in silico prediction of gene expression
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2012-11
Sub-type Article (original research)
DOI 10.1093/bioinformatics/bts529
Volume 28
Issue 21
Start page 2789
End page 2796
Total pages 8
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2013
Language eng
Formatted abstract
Motivation: Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.

Results: In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using in vivo TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use no TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on in vivo TF binding data.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes First published online: 6 September 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 18 times in Thomson Reuters Web of Science Article | Citations
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