Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 856281, 6 pages
Research Article

Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach

Jia Song,1,2 Li Xu,1 and Hong Sun2

1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2Department of Electronic and Information Technology, Suzhou Vocational University, Suzhou 215104, China

Received 9 October 2012; Accepted 13 December 2012

Academic Editor: Yinglei Song

Copyright © 2013 Jia Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms, Caenorhabditis elegans and mus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations.