Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.
@article{Schroeder2010,
author = {Schr\"oder, Adrian and Eichner, Johannes and Supper, Jochen and Eichner,
Jonas and Wanke, Dierk and Henneges, Carsten and Zell, Andreas},
title = {{Predicting DNA-Binding Specificities of Eukaryotic Transcription
Factors}},
journal = {PLoS ONE},
publisher = {Public Library of Science},
year = {2010},
volume = {5},
pages = {e13876},
number = {11},
month = nov,
abstract = {Today, annotated amino acid sequences of more and more transcription
factors (TFs) are readily available. Quantitative information about
their DNA-binding specificities, however, are hard to obtain. Position
frequency matrices (PFMs), the most widely used models to represent
binding specificities, are experimentally characterized only for
a small fraction of all TFs. Even for some of the most intensively
studied eukaryotic organisms (i.e., human, rat and mouse), roughly
one-sixth of all proteins with annotated DNA-binding domain have
been characterized experimentally. Here, we present a new method
based on support vector regression for predicting quantitative DNA-binding
specificities of TFs in different eukaryotic species. This approach
estimates a quantitative measure for the PFM similarity of two proteins,
based on various features derived from their protein sequences. The
method is trained and tested on a dataset containing 1 239 TFs with
known DNA-binding specificity, and used to predict specific DNA target
motifs for 645 TFs with high accuracy.},
doi = {10.1371/journal.pone.0013876},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/schroederTFBSPrediction.pdf},
publisher = {Public Library of Science},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0013876}
}