Motif识别算法简介及软件性能研究  

Introduction of Algorithms and Performance Research of Softwares for Motif Discovery

作　　者：朱骥[1] 杨华[1] 牛北方[1] 郎显宇[1] 陆忠华[1] 迟学斌[1] 

机构地区：[1]中国科学院计算机网络信息中心超级计算中心

出　　处：《计算机应用研究》2006年第10期66-69,共4页Application Research of Computers

基　　金：国家"863"计划资助项目(2002AA104540)

摘　　要：Motif在转录和后转录水平的基因表达调控中起着重要的作用。目前,识别Motif的算法和相应的软件已有不少,但是却鲜有对各种算法及软件性能共同评测的研究和报告。介绍了算法的分类以及三种常见的Mo-tif识别算法W ordup,MM和G ibbs采样,并对A lignACE,MEME,MotifSampler,W eeder等13种Motif寻找软件进行性能比较分析。通过生物学意义的研究和性能比较结果可以得出:由于唯有W eeder算法考虑了Motif保守核心位置,因而它在各种软件中识别效果较好;大部分算法只考虑简单而且短的Motif,所以各种软件对酵母菌这种单细胞生物的Motif识别性能比多细胞生物要高。Motif plays a key role in the gene-expression regulating on both transcriptional and post-transcriptional levels. Nowadays there are several algorithms and softwares on detecting Motif, but, however, there is few papers on comparing the performance of these algorithms and softwares. This paper comes up with this background to introduce the classification of the algorithms in general and three common algorithms： Wordup, MM, Gibbs sampling-in details. And a performance comparison is made on the thirteen softwares for Motif detecting such as AlignACE, MEME, MotifSampler, Weeder, etc. Based on the biological research and the performance report, this paper ends with a conclusion that Weeder is the most effective one of these softwares, for it is the only algorithm that takes account of the conserved core positions of Motifs ; Most algorithms only consider simple and short Motifs, so their Motif detecting performance on monadic yeast is significantly higher than on metazoans.

关 键 词：MOTIF Wordup MM GIBBS采样 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]