Mining yeast transcriptional regulatory modules from factor DNA-binding sites and gene expression data

In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Different combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology. In this study, we first defined a new kind of motifs in regulatory networks, transcriptional regulatory modules (TRMs), with the form factorset --> geneset, which emphasizes the combinatorial gene control of the group of factors factorset on the group of genes geneset. Second, we developed an efficient method based on a closed itemset mining technique for finding the two most informative kinds of TRMs, closed inf-TRMs and closed sup-TRMs, from factor DNA-binding sites and gene expression profiles data. The set of all closed inf-TRMs and closed sup-TRMs is often orders of magnitude smaller than the set of all TRMs but does not loss any information. When being applied to yeast data, our method produced results that are more compact, concise and comprehensive than those from previous studies to identify and interpret the transcriptional role of regulator combinations on sets of genes.

Supplementary files: http://www.jaist.ac.jp/~h-pham/regulation/.