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

@article{citeulike:3808579, 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. 10.1093/bioinformatics/btn645}, author = {Alleyne, Trevis M. and Pena-Castillo, Lourdes and Badis, Gwenael and Talukder, Shaheynoor and Berger, Michael F. and Gehrke, Andrew R. and Philippakis, Anthony A. and Bulyk, Martha L. and Morris, Quaid D. and Hughes, Timothy R.}, citeulike-article-id = {3808579}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn645}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/8/1012}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19088121}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19088121}, day = {15}, doi = {10.1093/bioinformatics/btn645}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {eucariotic\_transcription\_regulation, genetic\_regulatory\_networks, modelling}, month = {April}, number = {8}, pages = {1012--1018}, posted-at = {2009-04-08 12:19:19}, priority = {2}, title = {Predicting the binding preference of transcription factors to individual DNA k-mers}, url = {http://dx.doi.org/10.1093/bioinformatics/btn645}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:3808579 L3 - citeulike-article-id:3808579 N2 - 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. 10.1093/bioinformatics/btn645 IS - 8 JF - Bioinformatics SN - 1460-2059 EP - 1018 TI - Predicting the binding preference of transcription factors to individual DNA k-mers VL - 25 SP - 1012 KW - eucariotic_transcription_regulation KW - genetic_regulatory_networks KW - modelling AU - Alleyne, Trevis AU - Pena-Castillo, Lourdes AU - Badis, Gwenael AU - Talukder, Shaheynoor AU - Berger, Michael AU - Gehrke, Andrew AU - Philippakis, Anthony AU - Bulyk, Martha AU - Morris, Quaid AU - Hughes, Timothy PY - 2009/04/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btn645 ER -