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

Open Access Research

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment

Jakob Hedegaard1*, Cristina Arce2†, Silvio Bicciato3†, Agnès Bonnet4†, Bart Buitenhuis1†, Melania Collado-Romero2†, Lene N Conley1†, Magali SanCristobal4†, Francesco Ferrari5†, Juan J Garrido2†, Martien AM Groenen6†, Henrik Hornshøj1†, Ina Hulsegge7†, Li Jiang1†, Ángeles Jiménez-Marín2†, Arun Kommadath7†, Sandrine Lagarrigue8†, Jack AM Leunissen9†, Laurence Liaubet4†, Pieter BT Neerincx9†, Haisheng Nie6†, Jan Poel6†, Dennis Prickett10†, María Ramirez-Boo2†, Johanna MJ Rebel11†, Christèle Robert-Granié12†, Axel Skarman1†, Mari A Smits7†, Peter Sørensen1†, Gwenola Tosser-Klopp4† and Michael Watson10†

Author Affiliations

1 Aarhus University, Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, P.O. Box 50 DK-8830 Tjele, Denmark

2 Grupo de Genómica y Mejora Animal, Departamento de Genética, Facultad de Veterinaria, Universidad de Córdoba, Campus de Rabanales, Edificio C-5, 14071 Córdoba, Spain

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

4 Laboratoire de Génétique Cellulaire, INRA, UMR444, F-31326 Castanet-Tolosan, France

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

6 Animal Breeding and Genomics Centre, Wageningen University, Wageningen UR, P.O. Box 338, 6700 AH, Wageningen, The Netherlands

7 Animal Breeding and Genomics Centre, Animal Sciences Group, Wageningen UR, P.O. Box 65, 8200 AB Lelystad, The Netherlands

8 INRA, Agrocampus Ouest, UMR598 Génétique animale, F-35000 Rennes, France

9 Laboratory of Bioinformatics, Wageningen University, Wageningen UR, P.O. Box 569, 6700 AN, Wageningen, The Netherlands

10 Institute for Animal Health, Compton, nr Newbury, RG20 7NN, UK

11 Centraal Veterinair Instituut van Wageningen UR, Postbox 65, 8200 AB Lelystad, The Netherlands

12 Station d'Amélioration Génétique des Animaux, INRA, UR631, F-31326 Castanet-Tolosan, France

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BMC Proceedings 2009, 3(Suppl 4):S5 doi:

Published: 16 July 2009

Abstract

Background

The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.

Results

Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.

Conclusion

It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.