Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data

Bioinformatics
Benedikt ZacherHolger Fröhlich

Abstract

There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse. In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer. The method is impleme...Continue Reading

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Citations

Aug 27, 2013·Nucleic Acids Research·Steffen SassFabian J Theis
Feb 11, 2014·Journal of Molecular Medicine : Official Organ of the Gesellschaft Deutscher Naturforscher Und Ärzte·Umar RazaOzgür Sahin
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Sep 3, 2014·Journal of Biomedical Informatics·Junpeng ZhangJiuyong Li
Jul 14, 2014·Briefings in Bioinformatics·Thuc Duy LeJiuyong Li
Nov 25, 2020··Duc DoSerdar Bozdag

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