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Bioinformatics Advance Access published online on July 10, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm348
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An Approach to Predict Transcription Factor DNA Binding Site Specificity Based upon Gene and Transcription Factor Functional Categorization

Ziliang Qian 2,3,$, Lingyi Lu 2,3,$, XiaoJun Liu 7, Yu-Dong Cai 1,4,* and Yixue Li 5,3,6,*

1CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
2Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China
3Bioinformatics Center, Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
4Department of Mathematics, University of Manchester, Institute of Science and Technology, P.O. Box 88, Manchester M60 1QD, UK
5Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, 200235 Shanghai, China
6College of Life Science & Biotechnology, Shanghai Jiao Tong University
7Molecular Physiology Laboratory, Centre for Cardiovascular Science & Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh, EH16 4TJ, U.K.
$These authors contribute equally to this work.

*To whom correspondence should be addressed. Yu-Dong Cai: e-mail: cyd{at}picb.ac.cnYixue Li: yxli{at}sibs.ac.cn


  Abstract

Motivation: To understand transcription regulatory mechanisms, it is indispensable to investigate transcription factor (TF) DNA binding preferences. We noted that the generally acknowledged information of functional annotations of TFs as well as that of their target genes should provide useful hints in determining transcription factor DNA binding preferences.

Results: In this contribution, we developed an integrative method based on the Nearest Neighbor Algorithm, to predict DNA binding preferences through integrating both the functional/structural information of transcription factors and the interaction between transcription factors and their targets. The accuracy of cross validation tests on the dataset consisting of 3430 positive samples and 7000 negative samples reaches 87.0% for 10-fold cross-validation and 87.9% for jackknife cross validation test, which is a much better result than that in our previous work (Qian, et al., 2006). The prediction result indicates that the improved method we developed could be a powerful approach to infer the transcription factor DNA preference in silico.

Associate Editor: Prof. Alfonso Valencia

Received on December 24, 2006; revised on June 4, 2007; accepted on June 27, 2007

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