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Open Access Highly Accessed Methodology article

CLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species

Maurizio Pellegrino1, Paolo Provero2, Lorenzo Silengo1 and Ferdinando Di Cunto1*

Author Affiliations

1 Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy

2 Fondazione per le Biotecnologie, Viale Settimio Severo, Torino, Italy

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BMC Bioinformatics 2004, 5:179  doi:10.1186/1471-2105-5-179

Published: 19 November 2004



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. 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.


We 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.


CLOE 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.