Ludwig-Maximilians-Universität München, Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Chemistry and Biochemistry, Feodor-Lynen-Street 25, 81377 Munich, Germany.
Bioinformatics. 2012 Jul 1;28(13):1714-20. doi: 10.1093/bioinformatics/bts257. Epub 2012 May 4.
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 implemented in the R package birta, which is freely available for Bio-conductor (>=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html.
已经有许多成功的实验和生物信息学努力来阐明几个生物体中的转录因子（TF）-靶标网络。对于许多生物体，这些注释都辅以高质量的 miRNA-靶标网络。然而，尝试将这些网络与基因表达数据结合使用以得出关于 TF 或 miRNA 活性的结论的尝试仍然相对较少。
在这项研究中，我们提出了一种新的方法，即通过在特定条件下结合 miRNA 和 mRNA 表达数据进行转录活性调控的贝叶斯推断（BIRTA），来推断特定条件下的 TF 和 miRNA 活性。这意味着我们的模型通过特定 miRNAs 和 TFs 的活性来解释特定实验条件下的 mRNA 和 miRNA 表达，从而能够区分从活跃到不活跃（负开关）和从不活跃到活跃（正开关）形式的转换。我们的模型的广泛模拟表明，与其他方法相比，它具有良好的预测性能。此外，通过比较有氧和厌氧生长条件下的大肠杆菌数据以及胰腺和卵巢癌的人类表达数据，证明了 BIRTA 的实用性。
该方法在 R 包 birta 中实现，可在 http://www.bioconductor.org/packages/release/bioc/html/birta.html 上的 Bio-conductor（>=2.10）上免费使用。
