Predicting the binding preference of transcription factors to individual DNA k-mers
Name
Alleyne-2009-Predicting the binding preference of transcription.pdf
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Author(s)
Bulyk, Martha L.
Philippakis, Anthony A.
Alleyne, Trevis M.
Peña-Castillo, Lourdes
Badis, Gwenael
Talukder, Shaheynoor
Berger, Michael F.
Gehrke, Andrew R.
Morris, Quaid D.
Hughes, Timothy R.
Date Issued
December 2008
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Alleyne, T. M. et al. “Predicting the Binding Preference of Transcription Factors to Individual DNA K-mers.” Bioinformatics 25.8 (2008): 1012–1018.
Version
Final published version
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@utorotno.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
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Creative Commons Attribution Non-Commercial
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DOI of Published Version
https://doi.org/10.1093/bioinformatics/btn645
