This article is part of the supplement: Bioinformatics Methods for Biomedical Complex System Applications

Research

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

Luca Corradi^1*†, Valentina Mirisola^2,4†, Ivan Porro^1†, Livia Torterolo^1†, Marco Fato^1†, Paolo Romano^3† and Ulrich Pfeffer^4†

• * Corresponding author: Luca Corradi luca.corradi@unige.it

• † Equal contributors

Author Affiliations

¹ University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, 16145, Italy

² National Research Council, Institute of Electronics and Engineering of Information and Telecommunications, Torre di Francia, Via de Marini 6, 16149, Genoa, Italy

³ National Cancer Research Institute, Bioinformatics group, Largo Rosanna Benzi 10,16132, Genoa, Italy

⁴ National Cancer Research Institute, Functional Genomics, Largo Rosanna Benzi 10, 16132, Genoa, Italy

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BMC Bioinformatics 2009, 10(Suppl 12):S10 doi:10.1186/1471-2105-10-S12-S10

Published: 15 October 2009

Abstract

Background

Complex microarray gene expression datasets can be used for many independent analyses and are particularly interesting for the validation of potential biomarkers and multi-gene classifiers. This article presents a novel method to perform correlations between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools.

Results

We developed Survival Online (available at http://ada.dist.unige.it:8080/enginframe/bioinf/bioinf.xml webcite), a Web-based system that allows for the analysis of Affymetrix GeneChip microarrays by using a parallel version of dChip. The user is first enabled to select pre-loaded datasets or single samples thereof, as well as single genes or lists of genes. Expression values of selected genes are then correlated with sample annotation data by uni- or multi-variate Cox regression and survival analyses. The system was tested using publicly available breast cancer datasets and GO (Gene Ontology) derived gene lists or single genes for survival analyses.

Conclusion

The system can be used by bio-medical researchers without specific computation skills to validate potential biomarkers or multi-gene classifiers. The design of the service, the parallelization of pre-processing tasks and the implementation on an HPC (High Performance Computing) environment make this system a useful tool for validation on several independent datasets.