A framework for integrative analysis of transcriptional and post-transcriptional gene regulation

Gene regulation is a complex biological phenomenon. A reliable analysis of this process requires the integration of several data sources in a rigorous pipeline. Here, we propose an approach for simultaneous modeling of transcriptional and post-transcriptional gene regulation over a Bayesian module network. The framework uses paired samples of mRNA and microRNA expressions and their sequence data to produce low-level regulatory circuits in addition to the co-regulated entities of mRNAs, microRNAs and transcription factors. The experiments performed on a real cancer dataset reveal that several biologically meaningful clusters and motifs can be inferred. The results lead to the generation of some testable biological hypotheses.