Affiliations: INSERM U620, Faculté de Médecine/Pharmacie,
Rennes, France | IRMAR, Université de Rennes 1, Rennes,
France | INSERM U533, Faculté de médecine, Nantes,
France
Abstract: Microarray technology is a widely used approach to gene expression
analysis. Many tools for microarray management and data analysis have been
developed, and recently new methods have been proposed for deciphering
biological pathways by integrating microarray data with other data sources.
However, to improve microarray analysis and provide meaningful gene interaction
networks, integrated software solutions are still needed. Therefore, we
developed M@IA, an environment for DNA microarray data analysis allowing gene
network reconstruction. M@IA is a microarray integrated application which
includes all of the steps of a microarray study, from MIAME-compliant raw data
storage and processing gene expression analysis. Furthermore, M@IA allows
automatic gene annotation based on ontology, metabolic/signalling pathways,
protein interaction, miRNA and transcriptional factor associations, as well as
integrative analysis of gene interaction networks. Statistical and graphical
methods facilitate analysis, yielding new hypotheses on gene expression data.
To illustrate our approach, we applied M@IA modules to microarray data taken
from an experiment on liver tissue. We integrated differentially expressed
genes with additional biological information, thus identifying new molecular
interaction networks that are associated with fibrogenesis. M@IA is a new
application for microarray management and data analysis, offering functional
insights into microarray data by the combination of gene expression data and
biological knowledge annotation based on interactive graphs. M@IA is an
interactive multi-user interface based on a flexible modular architecture and
it is freely available for academic users at http://maia.genouest.org.
Keywords: Microarray, database, systems biology, KEGG, gene ontology
