Abstract
BackgroundExisting 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.ResultsWe 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.ConclusionsThese results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.
Highlights
Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes
Gene expression profiles are often based on weak similarities that are unlikely to correlate with true co-regulation [6]
G1/S cell cycle transition Gene expression during G1/S transition of the cell cycle in S. cerevisiae is regulated by two transcription factors, MBF and SBF (Mlu1 box and Swi4/6 cell cycle box binding factor, respectively)
Summary
Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. The approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions. DNA sequence motif finders are often used to predict potential regulatory motifs upstream of co-regulated genes, typically identified through gene expression experiments. The importance of upstream regulatory motifs for establishing a link between co-expression and co-regulation has been recognized previously [1,2,3] These motifs represent patterns in sequence data important both for transcriptional regulation and protein function prediction [4]. It is believed that similar gene expression profiles are the result of similar regulatory mechanisms [5]. Genes displaying similar expression profiles may respond to different external stimuli, represent parallel biosynthetic pathways, and/or be regulated by different transcription factors. The problem of elucidating functional relationships and identifying potential regulatory motifs among co-expressed genes is quite challenging
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