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Treelength Optimization for Phylogeny Estimation

机译:系统发育估计的树长优化

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摘要

The standard approach to phylogeny estimation uses two phases, in which the first phase produces an alignment on a set of homologous sequences, and the second phase estimates a tree on the multiple sequence alignment. POY, a method which seeks a tree/alignment pair minimizing the total treelength, is the most widely used alternative to this two-phase approach. The topological accuracy of trees computed under treelength optimization is, however, controversial. In particular, one study showed that treelength optimization using simple gap penalties produced poor trees and alignments, and suggested the possibility that if POY were used with an affine gap penalty, it might be able to be competitive with the best two-phase methods. In this paper we report on a study addressing this possibility. We present a new heuristic for treelength, called BeeTLe (Better Treelength), that is guaranteed to produce trees at least as short as POY. We then use this heuristic to analyze a large number of simulated and biological datasets, and compare the resultant trees and alignments to those produced using POY and also maximum likelihood (ML) and maximum parsimony (MP) trees computed on a number of alignments. In general, we find that trees produced by BeeTLe are shorter and more topologically accurate than POY trees, but that neither POY nor BeeTLe produces trees as topologically accurate as ML trees produced on standard alignments. These findings, taken as a whole, suggest that treelength optimization is not as good an approach to phylogenetic tree estimation as maximum likelihood based upon good alignment methods.
机译:系统发育估计的标准方法使用两个阶段,其中第一阶段在一组同源序列上产生比对,第二阶段在多序列比对中估计树。 POY是一种寻求最小化总树长的树/对齐对的方法,是此两阶段方法最广泛使用的替代方法。然而,在树长优化下计算出的树的拓扑精度是有争议的。尤其是,一项研究表明,使用简单的空位罚分进行树长优化会产生较差的树木和路线,并暗示了如果将POY与仿射空位罚分一起使用,则有可能与最佳的两阶段方法竞争。在本文中,我们报告了针对这种可能性的研究。我们提出了一种新的树长启发式方法,称为BeeTLe(更好的树长),可以保证产生至少与POY一样短的树。然后,我们使用这种启发式方法来分析大量的模拟和生物学数据集,并将生成的树和对齐方式与使用POY生成的树和对齐方式进行比较,并且还将根据多个对齐方式计算出的最大似然(ML)和最大简约(MP)树进行比较。通常,我们发现BeeTLe生成的树比POY树更短,并且在拓扑上更准确,但是POY和BeeTLe都没有像在标准路线上生成的ML树那样在拓扑上准确。从总体上看,这些发现表明,树长优化不如基于良好对齐方法的最大似然法那样好,是系统树估计的一种好方法。

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