基于吉布斯采样的TFBS识别算法研究

Research of Algorithm for Finding TFBS Based on Gibbs Sampling

计算机方法识别转录因子结合位点(TFBS,也称“模式”)是目前生物信息学的一个很有吸引性和挑战性的课题。吉布斯采样识别模式的算法本质上是一个启发式搜索方法,容易陷入非全局最优的局部最大值。为此,提出了一种改进的吉布斯采样策略YGMS(Yeast Gibbs Motif Sampler)来识别酿酒酵母共表达基因调控区域转录因子结合位点。在酵母的共调控基因序列的数据集测试中,YGMS比其他几个基于吉布斯采样算法更有效地识别出真实模式序列,在一定程度上提高了算法的性能。

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 non-optimal ＂local maximum＂. To overcome it and to improve the performance of the algorithm, an im proved Gibbs sampling strategy YGMS （Yeast Gibbs Motif Sampler） for finding motifs in gene sequences of yeast is present. YGMS and other existing Gibbs sampling algorithms were tested on real biological data sets with yeast regulatory elements. The results show that YGMS has better performance than other Gibbs sampling methods to a great extent in accuracy and sensitivity of finding true motifs.