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.
Department of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA. viktor.martyanov@dartmouth.edu
Viktor
V
Martyanov
Robert H
RH
Gross
D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods