Abstract
BackgroundPublic repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation.ResultsWe demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest.ConclusionsCLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.
Highlights
Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles
Predictive value of CLOE compared to single organisms and multiple species co-expression analysis We investigated the effectiveness of our approach, by comparing it to single and multiple organisms co-expression analysis
Even though in this case our approach and the multiple species comparison gave, on average, a higher percentage of compatible predictions, this was not dramatically different from the single-organism method. These results strongly suggest that, compared to the single organism approach, the highly reduced number of candidate partners produced by multiple organism coexpression analysis and CLOE is strongly enriched of genes characterized by more stringent functional relationships
Summary
Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The availability of genome sequences from several model organisms, including humans, and of high-throughput technologies to study gene function is dramatically changing the approach to biological problems. The study of gene function is further complicated by the alternative use of transcriptional units, frequently resulting in the production of proteins with different or even antagonistic activities from the same genes [2,3]. The extensive use of this technology by hundreds laboratories has resulted in the production of an enormous amount of data, many of which have been deposited in public databases [5,6]
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