ArticleBioinformatics (Oxford, England)2012
Genome-wide in silico prediction of gene expression.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 57 citations in OpenAlex.
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- A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.Briefings in bioinformatics · 2018Article
- RegulatorTrail: a web service for the identification of key transcriptional regulators.Nucleic acids research · 2017Article
- Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells.Oncotarget · 2017Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationModelling 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.
resultsIn 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. AVAILABILITY AND IMPLEMENTATION: All scripts, motifs and data presented in this article are available online at http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.