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BayGO: Bayesian analysis of ontology term enrichment in microarray data

Ricardo ZN Vêncio1,2*, Tie Koide3, Suely L Gomes3 and Carlos A de B Pereira1,4

Author Affiliations

1 BIOINFO-USP Núcleo de Pesquisas em Bioinformática, Universidade de São Paulo, Rua do Matão 1010, 05508-090 São Paulo, Brazil

2 Instituto Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein, Av. Albert Einstein 627, 05651-901 São Paulo, Brazil

3 Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, Av. Prof. Lineu Prestes 748, 05508-000 São Paulo, Brazil

4 Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão 1010, 05508-090 São Paulo, Brazil

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BMC Bioinformatics 2006, 7:86 doi:10.1186/1471-2105-7-86

Published: 23 February 2006

Abstract

Background

The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem.

Results

BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO webcite in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses.

Conclusion

The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.