Joint Bayesian Inference of miRNA and Transcription Factor Activities

Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors (TF) and post-transcriptional degradation by microRNAs (miRNA). birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to infer switches of regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure. In the model, mRNA expression levels depend on the activity states of its regulating miRNAs and TFs and the miRNA expression is dependent on the associated miRNA activity. birta uses Markov-Chain-Monte-Carlo (MCMC) sampling to infer these activity states, using one of the conditions as a reference. During MCMC, switch moves toggling the state of a regulator between active and inactive and swap moves exchanging the activitiy states of either two miRNAs or two TFs are used to sample from the posterior distribution. [10] This vignette presents the application of the birta package in different scenarios including a simulated and a real data set. The package can be loaded by typing: