EuroDia: a beta-cell gene expression resource

Abstract

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: http://eurodia.vital-it.ch.

Figure 1.

Web interface. (A) Experiments can be grouped by type, laboratory affiliation, project, organism or array design. For each category, experiment name and description are presented. Clicking on one experiment name reveals quality control plots (B) and the list of related hybridizations. (C) For each hybridization, quality control plots are provided. (D) Experiments can be downloaded in three forms: normalized data with probe annotation, raw data and Bioconductor expressionSet object.

Figure 2.

Experiment analysis. The order of the boxes reflects the flow of analysis steps. From top to bottom and left to right, the experiment name and description, the selection of the type of analysis, the form to describe analysis design (for this example, paired samples design), the list of differentially expressed probes with their relevant gene symbol, expression ratios and P-values of differential expression and the list of altered GO categories is provided.