Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

BACKGROUND: Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions. RESULTS: We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs. CONCLUSIONS: These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.