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

被引:4
作者
Corradi, Luca [1 ]
Mirisola, Valentina [2 ]
Porro, Ivan [1 ]
Torterolo, Livia [1 ]
Fato, Marco [1 ]
Romano, Paolo [3 ]
Pfeffer, Ulrich
机构
[1] Univ Genoa, Dept Commun Comp & Syst Sci, I-16145 Genoa, Italy
[2] CNR, Inst Elect Engn Informat & Telecommun, I-16149 Genoa, Italy
[3] Natl Inst Canc Res, Bioinformat Grp, I-16132 Genoa, Italy
关键词
MICROARRAY DATA; BREAST-CANCER; BIOINFORMATICS; MANAGEMENT; TOOL;
D O I
10.1186/1471-2105-10-S12-S10
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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), 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.
引用
收藏
页数:10
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