Open Access Technical Note

gViz, a novel tool for the visualization of co-expression networks

Raphaël Helaers13, Eric Bareke1*, Bertrand De Meulder1, Michael Pierre1, Sophie Depiereux1, Naji Habra2 and Eric Depiereux1

Author Affiliations

1 Bioinformatics and Biostatistics unit, Molecular Biology Research Unit (MBRU), Namur Center for Complex Systems (NAXYS), University of Namur (FUNDP), 61 Rue de Bruxelles, B-5000 Namur, Belgium

2 Research Center in Information Systems Engineering (PReCISE), Faculty of Computing, University of Namur (FUNDP), 21 Rue Grandgagnage, B-5000 Namur, Belgium

3 Laboratory of Human Molecular Genetics (GEHU), de Duve Institute, Catholic University of Louvain (UCLouvain), 75 Av. Hippocrate, B-1200 Brussels, Belgium

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BMC Research Notes 2011, 4:452  doi:10.1186/1756-0500-4-452

Published: 27 October 2011



The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs.


We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix.


We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.