An Improved Gibbs Sampling Algorithm for Finding TFBS

• Published in: Computational Intelligence and Security pp. 927 - 932
• Main Authors: He, Caisheng, Dai, Xianhua
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005
• Series: Lecture Notes in Computer Science
• Subjects: Background Model; Gibbs Sampling Algorithm; Motif Finding; Position Weight Matrix; Transcription Factor Binding Site
• ISBN: 9783540308188; 3540308180
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11596448_138

Computational methods detecting the transcription factor binding sites (TFBS) remain one of the most intriguing and challenging subjects in bioinformatics. Gibbs sampling is essentially a heuristic method, and it is easy to trap into a nonoptimal “local maximum”. To overcome this problem and to improve the accuracy and sensitivity of the algorithm, we present an improved Gibbs sampling strategy MPWMGMS to search for TFBS. We have tested MPWMGMS and other existing Gibbs sampling algorithms on simulated data and real biological data sets with regulatory elements. The results indicate that MPWMGMS has better performance than other methods to a great extent in accuracy and sensitivity of finding true TFBS.