PathEx: a novel multi factors based datasets selector web tool

Meta-analysis and gene set analysis of archived microarrays suggest implication of the spliceosome in metastatic and hypoxic phenotypes

We propose to make use of the wealth of underused DNA chip data available in public repositories to study the molecular mechanisms behind the adaptation of cancer cells to hypoxic conditions leading to the metastatic phenotype. We have developed new bioinformatics tools and adapted others to identify with maximum sensitivity those genes which are expressed differentially across several experiments. The comparison of two analytical approaches, based on either Over Representation Analysis or Functional Class Scoring, by a meta-analysis-based approach, led to the retrieval of known information about the biological situation - thus validating the model - but also more importantly to the discovery of the previously unknown implication of the spliceosome, the cellular machinery responsible for mRNA splicing, in the development of metastasis.

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

BACKGROUND

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.

FINDINGS

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.

CONCLUSIONS

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.