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.
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| http://dx.doi.org/10.1186/1471-2164-11-S2-S8 | DOI Listing |
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