Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

Gene Ontology term overlap as a measure of gene functional similarity

Meeta Mistry1 and Paul Pavlidis2*

Author Affiliations

1 CIHR/MSFHR Graduate Program in Bioinformatics, University of British Columbia, Canada

2 Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, British Columbia, Canada

For all author emails, please log on.

BMC Bioinformatics 2008, 9:327  doi:10.1186/1471-2105-9-327

Published: 4 August 2008

Abstract

Background

The availability of various high-throughput experimental and computational methods allows biologists to rapidly infer functional relationships between genes. It is often necessary to evaluate these predictions computationally, a task that requires a reference database for functional relatedness. One such reference is the Gene Ontology (GO). A number of groups have suggested that the semantic similarity of the GO annotations of genes can serve as a proxy for functional relatedness. Here we evaluate a simple measure of semantic similarity, term overlap (TO).

Results

We computed the TO for randomly selected gene pairs from the mouse genome. For comparison, we implemented six previously reported semantic similarity measures that share the feature of using computation of probabilities of terms to infer information content, in addition to three vector based approaches and a normalized version of the TO measure. We find that the overlap measure is highly correlated with the others but differs in detail. TO is at least as good a predictor of sequence similarity as the other measures. We further show that term overlap may avoid some problems that affect the probability-based measures. Term overlap is also much faster to compute than the information content-based measures.

Conclusion

Our experiments suggest that term overlap can serve as a simple and fast alternative to other approaches which use explicit information content estimation or require complex pre-calculations, while also avoiding problems that some other measures may encounter.