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Bioinformatics Advance Access originally published online on December 16, 2008
Bioinformatics 2009 25(8):1012-1018; doi:10.1093/bioinformatics/btn645
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© 2008 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-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Predicting the binding preference of transcription factors to individual DNA k-mers

Trevis M. Alleyne 1, Lourdes Peña-Castillo 2, Gwenael Badis 2, Shaheynoor Talukder 1, Michael F. Berger 3,4, Andrew R. Gehrke 3, Anthony A. Philippakis 3,4,5, Martha L. Bulyk 3,4,5,6, Quaid D. Morris 1,2 and Timothy R. Hughes 1,2,*

1Department of Molecular Genetics, 2Banting and Best Department of Medical Research, University of Toronto, Toronto, ON M5S 3E1, Canada, 3Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, 4Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, MA 02138, 5Harvard/MIT Division of Health Sciences and Technology (HST), Harvard Medical School, Boston, MA 02115 and 6Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members.

Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families.

Contact: t.hughes{at}utorotno.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on August 10, 2008; revised on November 16, 2008; accepted on December 11, 2008

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