树在多项式时间内的比较

D. Bogdanowicz, K. Giaro, B. Wróbel
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引用次数: 102

摘要

当系统发育重建的结果不是一棵树,而是好几棵树时,树的度量允许找出重建的树彼此之间的距离。它们也允许评估重建的准确性,如果真树是已知的。TreeCmp实现了8个可以在多项式时间内计算任意(不仅仅是分叉)树的度量:4个用于无根树(匹配分割度量,我们最近提出,Robinson-Foulds, Path Difference, Quartet)和4个用于有根树(匹配簇,Robinson-Foulds簇,节点分裂和三重)。TreeCmp是第一个匹配分裂/集群指标的实现,也是第一个高效方便的节点分裂实现。它允许比较相对较大的树。我们提供了一个应用TreeCmp的例子,使用基于树之间归一化相似性的精度度量,比较了十种方法在多达5000个外部节点的树中进行系统发育重建的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TreeCmp: Comparison of Trees in Polynomial Time
When a phylogenetic reconstruction does not result in one tree but in several, tree metrics permit finding out how far the reconstructed trees are from one another. They also permit to assess the accuracy of a reconstruction if a true tree is known. TreeCmp implements eight metrics that can be calculated in polynomial time for arbitrary (not only bifurcating) trees: four for unrooted (Matching Split metric, which we have recently proposed, Robinson-Foulds, Path Difference, Quartet) and four for rooted trees (Matching Cluster, Robinson-Foulds cluster, Nodal Splitted and Triple). TreeCmp is the first implementation of Matching Split/Cluster metrics and the first efficient and convenient implementation of Nodal Splitted. It allows to compare relatively large trees. We provide an example of the application of TreeCmp to compare the accuracy of ten approaches to phylogenetic reconstruction with trees up to 5000 external nodes, using a measure of accuracy based on normalized similarity between trees.
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