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从四重奏中推算超级树和超级网络。

Imputing supertrees and supernetworks from quartets.

作者信息

Holland B, Conner Glenn, Huber Katharina, Moulton V

机构信息

Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand.

出版信息

Syst Biol. 2007 Feb;56(1):57-67. doi: 10.1080/10635150601167013.

Abstract

Inferring species phylogenies is an important part of understanding molecular evolution. Even so, it is well known that an accurate phylogenetic tree reconstruction for a single gene does not always necessarily correspond to the species phylogeny. One commonly accepted strategy to cope with this problem is to sequence many genes; the way in which to analyze the resulting collection of genes is somewhat more contentious. Supermatrix and supertree methods can be used, although these can suppress conflicts arising from true differences in the gene trees caused by processes such as lineage sorting, horizontal gene transfer, or gene duplication and loss. In 2004, Huson et al. (IEEE/ACM Trans. Comput. Biol. Bioinformatics 1:151-158) presented the Z-closure method that can circumvent this problem by generating a supernetwork as opposed to a supertree. Here we present an alternative way for generating supernetworks called Q-imputation. In particular, we describe a method that uses quartet information to add missing taxa into gene trees. The resulting trees are subsequently used to generate consensus networks, networks that generalize strict and majority-rule consensus trees. Through simulations and application to real data sets, we compare Q-imputation to the matrix representation with parsimony (MRP) supertree method and Z-closure, and demonstrate that it provides a useful complementary tool.

摘要

推断物种系统发育是理解分子进化的重要组成部分。即便如此,众所周知,单个基因的准确系统发育树重建并不总是必然对应于物种系统发育。一种普遍接受的应对此问题的策略是对多个基因进行测序;分析由此产生的基因集合的方法则更具争议性。可以使用超级矩阵和超级树方法,尽管这些方法可能会抑制由诸如谱系分选、水平基因转移或基因复制与丢失等过程导致的基因树真实差异所产生的冲突。2004年,休森等人(《IEEE/ACM计算生物学与生物信息学汇刊》1:151 - 158)提出了Z - 闭包方法,该方法可以通过生成超级网络而非超级树来规避此问题。在此,我们提出一种生成超级网络的替代方法,称为Q - 插补法。具体而言,我们描述了一种利用四重奏信息将缺失分类单元添加到基因树中的方法。随后,将所得的树用于生成共识网络,即推广严格共识树和多数规则共识树的网络。通过模拟以及对实际数据集的应用,我们将Q - 插补法与简约矩阵表示(MRP)超级树方法和Z - 闭包方法进行了比较,并证明它提供了一个有用的补充工具。

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