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Abstract
BackgroundThe 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.ResultsWe 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.ConclusionsSIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.
Highlights
The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes
Implementation To address the critical need for a platform for gene expression signature analysis, we have developed a collection of tools over the course of several years
We report on the development of a computational platform that combines these in a biologist-friendly interface, using the principles previously established
Summary
We have developed a public software platform SIGNATURE that simplifies gene expression signature analysis by providing an easy to use GenePattern interface on top of a complex infrastructure of analysis software and a signature database. To apply them to microarrays from other platforms, the probes would need to be converted to these U133 probe sets. To remove technical variation across one or more gene expression data sets. To assign a subtype to gene expression data using a previously developed model. To score the modules in gene expression data using a previously developed model. SIGNATURE includes modules that perform a range of analyses on gene expression data 1[24]2[31]3[25]4[32]5[33]. 1 2 3 3 4 5 we have had more limited success in converting signals from cDNA arrays, and have not tried applying these analyses to expression data from sequencing platforms. We believe the ability to apply these methods depends on the reproducibility of the expression signals across platforms
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