Svennblad Bodil, Britton Tom
Department of mathematics, Uppsala University.
Stat Appl Genet Mol Biol. 2007;6:Article35. doi: 10.2202/1544-6115.1313. Epub 2007 Dec 21.
Maximum Likelihood (ML) is used as a standard method for estimating divergence times in phylogenetic trees. The method is consistent and hence the precision can be improved by analyzing longer sequences. In this paper we show that the precision can be improved also by including more taxa to the existing tree. It is a theoretical study, complemented with simulations, showing that the gain in precision is faster with increasing sequence length than with increasing number of taxa. We further compare the results of estimating divergence times using Maximum Likelihood with the much faster and less complex estimation method of Mean Path Length (MPL), which works with the evolution model of Jukes-Cantor (1969). It is shown that MPL is as good as ML in estimating divergence times of nodes that are located near the root in the tree, but ML is better in estimating the divergence times of nodes lower down.
最大似然法(ML)被用作估计系统发育树中分歧时间的标准方法。该方法具有一致性,因此通过分析更长的序列可以提高精度。在本文中,我们表明通过在现有树中纳入更多分类单元也可以提高精度。这是一项理论研究,并辅以模拟,结果表明随着序列长度增加,精度提升速度比增加分类单元数量更快。我们进一步将使用最大似然法估计分歧时间的结果与速度快得多且复杂度更低的平均路径长度(MPL)估计方法的结果进行比较,MPL方法基于朱克斯 - 坎托(1969)的进化模型。结果表明,在估计树中靠近根部节点的分歧时间时,MPL与ML一样好,但在估计较低位置节点的分歧时间时,ML更好。