Genetics Selection Evolution

, 39:633 | Cite as

Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)

  • Florence Jaffrézic
  • Dirk-Jan de Koning
  • Paul J Boettcher
  • Agnès Bonnet
  • Bart Buitenhuis
  • Rodrigue Closset
  • Sébastien Déjean
  • Céline Delmas
  • Johanne C Detilleux
  • Peter Dovč
  • Mylène Duval
  • Jean-Louis Foulley
  • Jakob Hedegaard
  • Henrik Hornshøj
  • Ina Hulsegge
  • Luc Janss
  • Kirsty Jensen
  • Li Jiang
  • Miha Lavrič
  • Kim-Anh Lê Cao
  • Mogens Sandø Lund
  • Roberto Malinverni
  • Guillemette Marot
  • Haisheng Nie
  • Wolfram Petzl
  • Marco H Pool
  • Christèle Robert-Granié
  • Magali San Cristobal
  • Evert M van Schothorst
  • Hans-Joachim Schuberth
  • Peter Sørensen
  • Alessandra Stella
  • Gwenola Tosser-Klopp
  • David Waddington
  • Michael Watson
  • Wei Yang
  • Holm Zerbe
  • Hans-Martin Seyfert
Open Access
Research

Abstract

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

Keywords

quality control differentially expressed genes mastitis resistance microarray data normalisation 

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Copyright information

© INRA, EDP Sciences 2007

Authors and Affiliations

  • Florence Jaffrézic
    • 1
  • Dirk-Jan de Koning
    • 2
  • Paul J Boettcher
    • 3
  • Agnès Bonnet
    • 4
  • Bart Buitenhuis
    • 5
  • Rodrigue Closset
    • 6
  • Sébastien Déjean
    • 7
  • Céline Delmas
    • 8
  • Johanne C Detilleux
    • 9
  • Peter Dovč
    • 10
  • Mylène Duval
    • 8
  • Jean-Louis Foulley
    • 1
  • Jakob Hedegaard
    • 5
  • Henrik Hornshøj
    • 5
  • Ina Hulsegge
    • 11
  • Luc Janss
    • 5
  • Kirsty Jensen
    • 2
  • Li Jiang
    • 5
  • Miha Lavrič
    • 10
  • Kim-Anh Lê Cao
    • 7
    • 8
  • Mogens Sandø Lund
    • 5
  • Roberto Malinverni
    • 3
  • Guillemette Marot
    • 1
  • Haisheng Nie
    • 12
  • Wolfram Petzl
    • 13
  • Marco H Pool
    • 11
  • Christèle Robert-Granié
    • 8
  • Magali San Cristobal
    • 4
  • Evert M van Schothorst
    • 14
  • Hans-Joachim Schuberth
    • 15
  • Peter Sørensen
    • 5
  • Alessandra Stella
    • 3
  • Gwenola Tosser-Klopp
    • 4
  • David Waddington
    • 2
  • Michael Watson
    • 16
  • Wei Yang
    • 17
  • Holm Zerbe
    • 13
  • Hans-Martin Seyfert
    • 17
  1. 1.INRA, UR337, (INRA_J)Jouy-en-JosasFrance
  2. 2.Roslin Institute, (ROSLIN)RoslinUK
  3. 3.Parco Tecnologico Padano (PTP)LodiItaly
  4. 4.INRA, UMR444, (INRA_T)Castanet-TolosanFrance
  5. 5.University of Aarhus, (AARHUS)TjeleDenmark
  6. 6.University of Liège, (ULg2)LiègeBelgium
  7. 7.Université Paul Sabatier, (INRA_T)ToulouseFrance
  8. 8.INRA, UR631, (INRA_T)Castanet-TolosanFrance
  9. 9.Faculty of Veterinary MedicineUniversity of Liège, (ULg1)LiègeBelgium
  10. 10.University of Ljubljana, (SLN)Slovenia
  11. 11.Animal Sciences Group Wageningen URLelystadThe Netherlands
  12. 12.Wageningen University and Research Centre, (WUR)WageningenThe Netherlands
  13. 13.Ludwig-Maximilians-UniversityMunichGermany
  14. 14.RIKILT-Institute of Food Safety, (WUR)WageningenThe Netherlands
  15. 15.University of Veterinary MedicineHannoverGermany
  16. 16.Institute for Animal Health, (IAH)ComptonUK
  17. 17.Research Institute for the Biology of Farm AnimalsDummerstorfGermany

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