GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima

• Main Author: Shida Kazuhito
• Format: Article
• Language: English
• Published: BMC 2006-11-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/7/486

<p>Abstract</p> <p>Background</p> <p>Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sampling based technique for TFBS discovery is attempted through an approach that is widely known, but which has never been investigated before: reduction of the effect of local optima.</p> <p>Results</p> <p>To alleviate the vulnerability of Gibbs sampling to local optima trapping, we propose to combine a thermodynamic method, called simulated tempering, with Gibbs sampling. The resultant algorithm, GibbsST, is then validated using synthetic data and actual promoter sequences extracted from <it>Saccharomyces cerevisiae</it>. It is noteworthy that the marked improvement of the efficiency presented in this paper is attributable solely to the improvement of the search method.</p> <p>Conclusion</p> <p>Simulated tempering is a powerful solution for local optima problems found in pattern discovery. Extended application of simulated tempering for various bioinformatic problems is promising as a robust solution against local optima problems.</p>