This article is part of the supplement: Proceedings of the 23rd International Conference on Genome Informatics (GIW 2012)
Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks
Department of Bioinformatics, University Medical Center Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany
BMC Systems Biology 2012, 6(Suppl 2):S15 doi:10.1186/1752-0509-6-S2-S15Published: 12 December 2012
Transcriptional networks of higher eukaryotes are difficult to obtain. Available experimental data from conventional approaches are sporadic, while those generated with modern high-throughput technologies are biased. Computational predictions are generally perceived as being flooded with high rates of false positives. New concepts about the structure of regulatory regions and the function of master regulator sites may provide a way out of this dilemma.
We combined promoter scanning with positional weight matrices with a 4-genome conservativity analysis to predict high-affinity, highly conserved transcription factor (TF) binding sites and to infer TF-target gene relations. They were expanded to paralogous TFs and filtered for tissue-specific expression patterns to obtain a reference transcriptional network (RTN) as well as tissue-specific transcriptional networks (TTNs).
When validated with experimental data sets, the predictions done showed the expected trends of true positive and true negative predictions, resulting in satisfying sensitivity and specificity characteristics. This also proved that confining the network reconstruction to the 1% top-ranking TF-target predictions gives rise to networks with expected degree distributions. Their expansion to paralogous TFs enriches them by tissue-specific regulators, providing a reasonable basis to reconstruct tissue-specific transcriptional networks.
The concept of master regulator or seed sites provides a reasonable starting point to select predicted TF-target relations, which, together with a paralogous expansion, allow for reconstruction of tissue-specific transcriptional networks.