利用Robinson-Foulds距离从不一致多拷贝基因树推断物种树
Inferring Species Trees from Incongruent Multi-Copy Gene Trees Using the Robinson-Foulds Distance
摘要：
提出了一种从多拷贝基因树中推断物种树的新方法。我们的方法是基于Robinson-Foulds（RF）距离到多标记树（mul-trees）的推广，即，基因树，其中多个叶子可以具有相同的标签。与大多数使用基因树的系统发育方法不同，该方法不假设基因树不一致是由单一的特定生物过程引起的，例如基因复制和丢失，深度合并或横向基因转移。我们证明了计算两棵多树之间的RF距离是NP难的，但计算一棵多树和一棵单标号树之间的广义RF距离是容易的。出于这一观察，我们制定了RF超树问题的多树（MulRF），它需要一个集合的多树，并构建一个物种树，最大限度地减少总的RF距离输入多树。我们提出了一个快速的启发式算法的MulRF超树问题。模拟实验表明，MulRF方法产生更准确的物种树比基因树简约方法时，不一致性所造成的基因树错误，重复和损失，和/或横向基因转移。此外，MulRF启发式算法在包含数百棵树的数据集上运行得很快，这些树具有多达一百个分类群。
英文摘要：
We present a new method for inferring species trees from multi-copy gene trees. Our method is based on a generalization of the Robinson-Foulds (RF) distance to multi-labeled trees (mul-trees), i.e., gene trees in which multiple leaves can have the same label. Unlike most previous phylogenetic methods using gene trees, this method does not assume that gene tree incongruence is caused by a single, specific biological process, such as gene duplication and loss, deep coalescence, or lateral gene transfer. We prove that it is NP-hard to compute the RF distance between two mul-trees, but it is easy to calculate the generalized RF distance between a mul-tree and a singly-labeled tree. Motivated by this observation, we formulate the RF supertree problem for mul-trees (MulRF), which takes a collection of mul-trees and constructs a species tree that minimizes the total RF distance from the input mul-trees. We present a fast heuristic algorithm for the MulRF supertree problem. Simulation experiments demonstrate that the MulRF method produces more accurate species trees than gene tree parsimony methods when incongruence is caused by gene tree error, duplications and losses, and/or lateral gene transfer. Furthermore, the MulRF heuristic runs quickly on data sets containing hundreds of trees with up to a hundred taxa.
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