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SIGNATURE: A workbench for gene expression signature analysis

Jeffrey T Chang1*, Michael L Gatza2, Joseph E Lucas2, William T Barry23, Peyton Vaughn2 and Joseph R Nevins24

Author Affiliations

1 Department of Integrative Biology and Pharmacology University of Texas Health Science Center at Houston, Houston TX, USA

2 Institute for Genome Sciences and Policy Duke University and Duke University Medical Center, Durham NC, USA

3 Department of Biostatistics and Bioinformatics Duke University Medical Center, Durham NC, USA

4 Department of Molecular Genetics and Microbiology Duke University Medical Center, Durham NC, USA

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BMC Bioinformatics 2011, 12:443  doi:10.1186/1471-2105-12-443

Published: 14 November 2011

Abstract

Background

The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.

Results

We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.

Conclusions

SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/ webcite.