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使用具有分段线性间隙成本的新型组对组序列比对算法提高多序列比对的准确性。

Improvement in accuracy of multiple sequence alignment using novel group-to-group sequence alignment algorithm with piecewise linear gap cost.

作者信息

Yamada Shinsuke, Gotoh Osamu, Yamana Hayato

机构信息

Department of Computer Science, Graduate School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.

出版信息

BMC Bioinformatics. 2006 Dec 1;7:524. doi: 10.1186/1471-2105-7-524.

Abstract

BACKGROUND

Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy and speed. In the alignment of a family of protein sequences, global MSA algorithms perform better than local ones in many cases, while local ones perform better than global ones when some sequences have long insertions or deletions (indels) relative to others. Many recent leading MSA algorithms have incorporated pairwise alignment information obtained from a mixture of sources into their scoring system to improve accuracy of alignment containing long indels.

RESULTS

We propose a novel group-to-group sequence alignment algorithm that uses a piecewise linear gap cost. We developed a program called PRIME, which employs our proposed algorithm to optimize the well-defined sum-of-pairs score. PRIME stands for Profile-based Randomized Iteration MEthod. We evaluated PRIME and some recent MSA programs using BAliBASE version 3.0 and PREFAB version 4.0 benchmarks. The results of benchmark tests showed that PRIME can construct accurate alignments comparable to the most accurate programs currently available, including L-INS-i of MAFFT, ProbCons, and T-Coffee.

CONCLUSION

PRIME enables users to construct accurate alignments without having to employ pairwise alignment information. PRIME is available at http://prime.cbrc.jp/.

摘要

背景

多序列比对(MSA)是生物信息学中的一种有用工具。尽管已经开发了许多MSA算法,但在准确性和速度方面仍有改进的空间。在蛋白质序列家族的比对中,全局MSA算法在许多情况下比局部算法表现更好,而当一些序列相对于其他序列有长插入或缺失(indels)时,局部算法比全局算法表现更好。许多最近领先的MSA算法已将从多种来源混合获得的两两比对信息纳入其评分系统,以提高包含长indels的比对的准确性。

结果

我们提出了一种使用分段线性空位罚分的新型组对组序列比对算法。我们开发了一个名为PRIME的程序,该程序采用我们提出的算法来优化定义明确的两两比对得分。PRIME代表基于轮廓的随机迭代方法。我们使用BAliBASE 3.0版和PREFAB 4.0版基准评估了PRIME和一些最近的MSA程序。基准测试结果表明,PRIME可以构建与目前最准确的程序相当的准确比对,包括MAFFT的L-INS-i、ProbCons和T-Coffee。

结论

PRIME使用户无需使用两两比对信息就能构建准确的比对。可在http://prime.cbrc.jp/获取PRIME。

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