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Cecilia Holm

Research team manager

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EuroDia : a beta-cell gene expression resource.


  • Robin Liechti
  • Gábor Csárdi
  • Sven Bergmann
  • Frédéric Schütz
  • Thierry Sengstag
  • Sylvia F. Boj
  • Joan Marc Servitja
  • Jorge Ferrer
  • Leentje Van Lommel
  • Frans Schuit
  • Sonia Klinger
  • Bernard Thorens
  • Najib Naamane
  • Decio L. Eizirik
  • Lorella Marselli
  • Marco Bugliani
  • Piero Marchetti
  • Stephanie Lucas
  • Cecilia Holm
  • C. Victor Jongeneel
  • Ioannis Xenarios

Summary, in English

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms. Database URL:


  • Molecular Endocrinology
  • EXODIAB: Excellence of Diabetes Research in Sweden

Publishing year





Database: the journal of biological databases and curation



Document type

Journal article


Oxford University Press


  • Medical Genetics



Research group

  • Molecular Endocrinology


  • ISSN: 1758-0463