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Bioinformatics 2006 22(14):e117-e123; doi:10.1093/bioinformatics/btl260
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Dense subgraph computation via stochastic search: application to detect transcriptional modules

Logan Everett 1, Li-San Wang 2 and Sridhar Hannenhalli *

1 Penn Center for Bioinformatics, University of Pennsylvania Philadelphia, PA, USA 19104
2 Department of Biology, University of Pennsylvania Philadelphia, PA, USA 19104

*To whom correspondence should be addressed.

Motivation: In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors—a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed.

Result: Here we report a generic stochastic search based method to compute dense subgraphs in a graph with an arbitrary number of partitions and an arbitrary connectivity among the partitions. We then use the tool to explore tissue-specific transcriptional regulation in the human genome. We validate our findings in Skeletal muscle based on literature. We could accurately deduce biological processes for transcription factors via the tri-partite clusters of transcription factors, genes, and the functional annotation of genes. Additionally, we propose a few previously unknown TF-pathway associations and tissue-specific roles for certain pathways. Finally, our combined analysis of Cardiac, Skeletal, and Smooth muscle data recapitulates the evolutionary relationship among the three tissues.

Contact: sridharh{at}pcbi.upenn.edu


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