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GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

André Fujita12, João R Sato2, Carlos E Ferreira2 and Mari C Sogayar1*

Author Affiliations

1 Chemistry Institute, University of São Paulo, Av. Lineu Prestes, 748 – São Paulo, 05508-900, SP, Brazil

2 Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010 – São Paulo, 05508-090, SP, Brazil

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BMC Bioinformatics 2007, 8:457  doi:10.1186/1471-2105-8-457

Published: 19 November 2007

Abstract

Background

Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.

Results

Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.

Conclusion

GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.