Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

<p>Abstract</p> <p>Background</p> <p>In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.</p> <p>Results</p> <p>We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure.</p> <p>Conclusion</p> <p>Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.</p

Network Analysis and Integration in a Virtual Cell Environment

Sommer B. Network Analysis and Integration in a Virtual Cell Environment. In: Chen M, Hofestädt R, eds. Approaches in Integrative Bioinformatics. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014: 275-297