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Bioinformatics Advance Access published online on September 16, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti038
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received June 19, 2004
Revised August 31, 2004
Accepted September 7, 2004

Article

Expression-based monitoring of transcription factor activity: The TELiS database

Steve W. Cole 1*, Weihong Yan 2, Zoran Galic 3, Jesusa Arevalo 4, and Jerome A. Zack 5

1 UCLA Department of Medicine, Los Angeles, CA, USA; the UCLA AIDS Institute, Los Angeles, CA, USA; the Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA; the UCLA Molecular Biology Institute, Los Angeles, CA, USA
2 UCLA Department of Chemistry and Biochemistry, Los Angeles, CA, USA
3 UCLA Department of Medicine, Los Angeles, CA, USA; the UCLA AIDS Institute, Los Angeles, CA, USA
4 UCLA Department of Medicine, Los Angeles, CA, USA
5 UCLA Department of Medicine, Los Angeles, CA, USA; UCLA Department of Microbiology, Immunology, and Molecular Genetics, Los Angeles, CA, USA; the UCLA AIDS Institute, Los Angeles, CA, USA; the Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA; the UCLA Molecular Biology Institute, Los Angeles, CA, USA

* To whom correspondence should be addressed. E-mail: coles{at}ucla.edu.


  Abstract

Motivation: In microarray studies it is often of interest to identify upstream transcription control pathways mediating observed changes in gene expression. The Transcription Element Listening System (TELiS) combines sequence-based analysis of gene regulatory regions with statistical prevalence analyses to identify transcription-factor binding motifs (TFBMs) that are over-represented among the promoters of up- or down-regulated genes. Efficiency is maximized by decomposing the problem into two steps: 1) a pirori compilation of prevalence matrices specifying the number of putative binding sites for a variety of transcription factors in promoters from all genes assayed by a given microarray, and 2) real-time statistical analysis of pre-compiled prevalence matrices to identify TFBMs that are over- or under-represented in promoters of an arbitrary set of differentially expressed genes. These interlocking JAVA applications PromoterScan and PromoterStats carry out these tasks, and together constitute the TELiS database for reverse inference of transcription factor activity.

Results: In two validation studies, TELiS accurately detected in vivo activation of NF-{kappa}B and the Type I interferon system by HIV-1 infection and pharmacologic activation of the glucocorticoid receptor in peripheral blood mononuclear cells. The population-based statistical inference underlying TELiS out-performed conventional statistical tests in analytic sensitivity, with parametric studies demonstrating accurate identification of transcription factor activity from as few as 20 differentially expressed genes. TELiS thus provides a simple, rapid, and sensitive tool for identifying transcription control pathways mediating observed gene expression dynamics.

Availability: http://www.telis.ucla.edu.


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