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Nucleic Acids Research Advance Access published online on October 25, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm885
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-com

Computational Biology

TREMOR—a tool for retrieving transcriptional modules by incorporating motif covariance

Larry N. Singh, Li-San Wang and Sridhar Hannenhalli*

Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA

* To whom correspondence should be addressed. Tel: +1 215 746 8683; Fax: +1 215 573 3111; Email: sridharh{at}pcbi.upenn.edu

Received July 19, 2007. Revised October 2, 2007. Accepted October 2, 2007.

A transcriptional module (TM) is a collection of transcription factors (TF) that as a group, co-regulate multiple, functionally related genes. The task of identifying TMs poses an important biological challenge. Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification. A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family. One problem with this approach is that closely related transcription factors can still target sufficiently distinct genes in a biologically meaningful way, and thus, pre-selecting a single family representative may in principle miss certain TMs. Here we report a method—TREMOR (Transcriptional Regulatory Module Retriever). This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives. The application of TREMOR on human muscle-specific, liver-specific and cell-cycle-related genes reveals TFs and TMs that were validated from literature and also reveals additional related genes.


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