Integration of known transcription factor binding site information and gene expression data to advance from co-expression to co-regulation.
The common approach to find co-regulated genes is to cluster genes based on gene expression. However, due to the limited information present in any dataset, genes in the same cluster might be co-expressed but not necessarily co-regulated. In this paper, we propose to integrate known transcription factor binding site information and gene expression data into a single clustering scheme. This scheme will find clusters of co-regulated genes that are not only expressed similarly under the measured conditions, but also share a regulatory structure that may explain their common regulation. We demonstrate the utility of this approach on a microarray dataset of yeast grown under different nutrient and oxygen limitations. Our integrated clustering method not only unravels many regulatory modules that are consistent with current biological knowledge, but also provides a more profound understanding of the underlying process. The added value of our approach, compared with the clustering solely based on gene expression, is its ability to uncover clusters of genes that are involved in more specific biological processes and are evidently regulated by a set of transcription factors.
Clements
Maarten
M
Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2600 GA Delft, the Netherlands. m.clements@tudelft.nl
van Someren
Eugene P
EP
Knijnenburg
Theo A
TA
Reinders
Marcel J T
MJ
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001665:Binding Sites; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000235:genetics*; D005809:Genes, Regulator; D011485:Protein Binding; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D020539:Sequence Analysis, Protein; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors