# Predicting the Binding Preference of Transcription Factors to Individual DNA $$\kappa$$-mers

 Title: Predicting the Binding Preference of Transcription Factors to Individual DNA $$\kappa$$-mers Author: Alleyne, Trevis M.; Peña-Castillo, Lourdes; Badis, Gwenael; Talukder, Shaheynoor; Berger, Michael F.; Gehrke, Andrew R.; Morris, Quaid D.; Hughes, Timothy R.; Philippakis, Anthony Andrew; Bulyk, Martha Leonia Note: Order does not necessarily reflect citation order of authors. Citation: Alleyne, Trevis M., Lourdes Peña-Castillo, Gwenael Badis, Shaheynoor Talukder, Michael F. Berger, Andrew R. Gehrke, Anthony A. Philippakis, Martha L. Bulyk, Quaid D. Morris, and Timothy R. Hughes. 2009. Predicting the binding preference of transcription factors to individual DNA $$\kappa$$-mers. Bioinformatics 25(8): 1012-1018. Full Text & Related Files: 2666811.pdf (434.2Kb; PDF) 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 $$\kappa$$-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. Published Version: doi://10.1093/bioinformatics/btn645 Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2666811/pdf/ Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10139267 Downloads of this work: