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通过使用更好的引导树改进多序列比对。

Improving multiple sequence alignment by using better guide trees.

作者信息

Zhan Qing, Ye Yongtao, Lam Tak-Wah, Yiu Siu-Ming, Wang Yadong, Ting Hing-Fung

出版信息

BMC Bioinformatics. 2015;16 Suppl 5(Suppl 5):S4. doi: 10.1186/1471-2105-16-S5-S4. Epub 2015 Mar 18.

Abstract

Progressive sequence alignment is one of the most commonly used method for multiple sequence alignment. Roughly speaking, the method first builds a guide tree, and then aligns the sequences progressively according to the topology of the tree. It is believed that guide trees are very important to progressive alignment; a better guide tree will give an alignment with higher accuracy. Recently, we have proposed an adaptive method for constructing guide trees. This paper studies the quality of the guide trees constructed by such method. Our study showed that our adaptive method can be used to improve the accuracy of many different progressive MSA tools. In fact, we give evidences showing that the guide trees constructed by the adaptive method are among the best.

摘要

渐进式序列比对是多序列比对最常用的方法之一。大致来说,该方法首先构建一棵引导树,然后根据树的拓扑结构逐步比对序列。人们认为引导树对渐进式比对非常重要;更好的引导树会给出更高准确性的比对结果。最近,我们提出了一种构建引导树的自适应方法。本文研究了用这种方法构建的引导树的质量。我们的研究表明,我们的自适应方法可用于提高许多不同的渐进式多序列比对工具的准确性。事实上,我们给出的证据表明,由自适应方法构建的引导树是最好的之一。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b6f6/4402577/689cd7fac812/1471-2105-16-S5-S4-1.jpg

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