Software

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states

Gaëlle Lelandais^1,2*, Philippe Marc^1,3, Pierre Vincens², Claude Jacq¹ and Stéphane Vialette¹

• * Corresponding author: Gaëlle Lelandais lelandais@biologie.ens.fr

Author Affiliations

¹ Laboratoire de Génétique Moléculaire CNRS UMR8541, Ecole Normale Supérieure, Paris, 75230 Cedex 05, France

² Equipe de Bioinformatique Génomique et Moléculaire INSERM E346, Université Paris 7, Paris, 75231 Cedex 05, France

³ Present address: Lipper Center for Computational Genetics and Department of Genetics, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA

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BMC Bioinformatics 2004, 5:20 doi:10.1186/1471-2105-5-20

Published: 3 March 2004

Abstract

Background

Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions.

Results

MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at http://www.transcriptome.ens.fr/micovito/ webcite.

Conclusions

MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables.