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Nucleic Acids Research 2005 33(Web Server Issue):W592-W595; doi:10.1093/nar/gki484
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© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

T-profiler: scoring the activity of predefined groups of genes using gene expression data

André Boorsma, Barrett C. Foat1, Daniel Vis, Frans Klis and Harmen J. Bussemaker1,2,*

Swammerdam Institute for Life Sciences–Microbiology, University of Amsterdam, Biocentrum Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands 1Department of Biological Sciences, Columbia University New York, NY 10027, USA 2Center for Computational Biology and Bioinformatics, Columbia University New York, NY 10032, USA

*To whom correspondence should be addressed. Tel: +1 212 854 9932; Fax: +1 212 865 8246; Email: Harmen.Bussemaker{at}columbia.edu

Received February 9, 2005. Revised April 18, 2005. Accepted April 18, 2005.

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. Here we describe T-profiler, a tool that uses the t-test to score changes in the average activity of predefined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif or location on the same chromosome. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can upload their microarray data for analysis on the web at http://www.t-profiler.org.


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