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Bioinformatics Advance Access published online on May 8, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm249
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

STOP: searching for Transcription Factor motifs using gene expression

Libi Hertzberg 1,2, Shai Izraeli 1 and Eytan Domany 2,*

1Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Sheba Medical Center, Tel Hashomer Israel and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
2Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel

*To whom correspondence should be addressed. Eytan Domany, E-mail: eytan.domany{at}weizmann.ac.il


  Abstract

Motivation: Existing computational methods that identify transcription factor (TF) binding sites on a gene's promoter are plagued by significant inaccuracies. Binding of a TF to a particular sequence is assessed by comparing its similarity score, obtained from the TF's known position weight matrix (PWM), to a threshold. If the similarity score is above the threshold, the sequence is considered a putative binding site. Determining this threshold is a central part of the problem, for which no satisfactory biologically based solution exists.

Results: We present here a method that integrates gene expression data with sequence-based scoring of TF binding sites, for determining a global score threshold for each TF. We validate our method, STOP (Searching TFs Of Promoters), in several ways: 1) We calculate the average expression values of groups of human putative target genes of each TF, and compare them to similar averages derived for random gene groups. The groups of putative targets show significantly higher relative average expression. 2) We find high consistency between the induced lists of putative targets in human and in mouse. 3) The expression patterns associated with human and mouse genes (ordered by PWM scores for each TF) exhibit high similarity between human and mouse, indicating that our method has firm biological basis. 4) Comparison of results obtained by STOP and PRIMA (Elkon, et al., 2003) suggests that determining the score threshold using gene expression, as is done in STOP, is more biologically tuned.

Availability: Software package will be available for academic users upon request.

Associate Editor: Dr. Limsoon Wong

Received on December 14, 2006; revised on May 2, 2007; accepted on May 2, 2007

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