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This article is part of the supplement: EADGENE and SABRE Post-analyses Workshop

Open Access Research

Microarray data mining using Bioconductor packages

Haisheng Nie1, Pieter BT Neerincx2, Jan van der Poel1, Francesco Ferrari3, Silvio Bicciato4, Jack AM Leunissen2 and Martien AM Groenen1*

Author Affiliations

1 Animal Breeding and Genomics Centre, Wageningen University, Marijkeweg 40, P.O. Box 338, 6700 AH, Wageningen, The Netherlands

2 Laboratory of Bioinformatics, Wageningen University, Dreijenlaan 3, P.O. Box 569, 6700 AN, Wageningen, The Netherlands

3 Department of Biology, University of Padova, Via G. Colombo 3, 35121, Padova, Italy

4 Department of Biomedical Sciences, University of Modena and Reggio Emilia, via Campi 287, 41100, Modena, Italy

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BMC Proceedings 2009, 3(Suppl 4):S9  doi:10.1186/1753-6561-3-S4-S9

Published: 16 July 2009

Abstract

Background

This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge.

Results

GO enrichment analysis identified significant (raw p-value < 0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value < 0.01).

Conclusion

Overall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.