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Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

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  • Adrian Schröder
  • Johannes Eichner
  • Jochen Supper
  • Jonas Eichner
  • Dierk Wanke
  • Carsten Henneges
  • Andreas Zell

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.

Suggested Citation

  • Adrian Schröder & Johannes Eichner & Jochen Supper & Jonas Eichner & Dierk Wanke & Carsten Henneges & Andreas Zell, 2010. "Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0013876
    DOI: 10.1371/journal.pone.0013876
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    References listed on IDEAS

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    1. Barbara E Engelhardt & Michael I Jordan & Kathryn E Muratore & Steven E Brenner, 2005. "Protein Molecular Function Prediction by Bayesian Phylogenomics," PLOS Computational Biology, Public Library of Science, vol. 1(5), pages 1-1, October.
    2. Shaun Mahony & Philip E Auron & Panayiotis V Benos, 2007. "DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-14, March.
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