Data from: Practical performance of tree comparison metrics

The phylogenetic literature contains numerous measures for assessing differences between two phylogenetic trees. Individual measures have been criticized on various grounds, but little is known about their comparative performance in typical applications. We evaluate the performance of nine tree distance measures on two tasks: (1) distinguishing trees separated by lesser versus greater numbers of recombinations, and (2) distinguishing trees inferred with lower versus higher quality data. We find that when the trees being compared are similar, measures which make use of branch lengths are superior, with the branch-length version of the Robinson-Foulds metric (Robinson & Foulds, 1979) performing best. In contrast, for dissimilar trees topology-only measures are superior, with the Alignment metric of Nye et al. (2006) performing best. We also apply the measures to a mammalian data set and observe that the best metric depends on whether branch-length information is of interest. We give practical recommendations for choosing a tree distance metric in different applications.系统发育学文献中已提出诸多用于评估两棵系统发育树（phylogenetic trees）之间差异的度量方法。各类单一度量方法曾因多种缘由受到质疑，但目前针对这类度量在典型应用场景中的相对性能，相关研究仍较为匮乏。我们对9种树距离度量（tree distance measures）方法开展性能评估，评估覆盖两项任务：(1) 区分经不同数量重组事件（recombinations）分隔的系统发育树；(2) 区分使用低质量与高质量数据推断得到的系统发育树。研究发现，当待比较的系统发育树较为相似时，利用分支长度（branch lengths）信息的度量方法表现更优，其中罗宾逊-福尔斯度量（Robinson-Foulds metric）的分支长度版本（Robinson & Foulds, 1979）效果最佳。与之相反，当待比较的系统发育树差异较大时，仅拓扑结构的度量方法（topology-only measures）表现更优，其中Nye等人（2006）提出的比对度量（Alignment metric）效果最佳。我们还将这些度量方法应用于一套哺乳动物数据集（mammalian data set），并观察到最优度量的选择取决于是否需要关注分支长度信息。针对不同应用场景下如何选择树距离度量方法，我们给出了实用建议。