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分子系统发育最大似然法的性质与效率

Property and efficiency of the maximum likelihood method for molecular phylogeny.

作者信息

Saitou N

机构信息

Center for Demographic and Population Genetics, University of Texas Health Science Center, Houston 77225.

出版信息

J Mol Evol. 1988;27(3):261-73. doi: 10.1007/BF02100082.

Abstract

The maximum likelihood (ML) method for constructing phylogenetic trees (both rooted and unrooted trees) from DNA sequence data was studied. Although there is some theoretical problem in the comparison of ML values conditional for each topology, it is possible to make a heuristic argument to justify the method. Based on this argument, a new algorithm for estimating the ML tree is presented. It is shown that under the assumption of a constant rate of evolution, the ML method and UPGMA always give the same rooted tree for the case of three operational taxonomic units (OTUs). This also seems to hold approximately for the case with four OTUs. When we consider unrooted trees with the assumption of a varying rate of nucleotide substitution, the efficiency of the ML method in obtaining the correct tree is similar to those of the maximum parsimony method and distance methods. The ML method was applied to Brown et al.'s data, and the tree topology obtained was the same as that found by the maximum parsimony method, but it was different from those obtained by distance methods.

摘要

研究了利用DNA序列数据构建系统发育树(包括有根树和无根树)的最大似然(ML)方法。尽管在比较每个拓扑结构条件下的ML值时存在一些理论问题,但可以通过启发式论证来证明该方法的合理性。基于这一论证,提出了一种估计ML树的新算法。结果表明,在进化速率恒定的假设下,对于三个操作分类单元(OTU)的情况,ML方法和UPGMA总是给出相同的有根树。对于四个OTU的情况,这似乎也大致成立。当我们在核苷酸替换速率变化的假设下考虑无根树时,ML方法在获得正确树方面的效率与最大简约法和距离法相似。将ML方法应用于布朗等人的数据,得到的树拓扑结构与最大简约法得到的相同,但与距离法得到的不同。

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