arrow
Return

Imputing supertrees and supernetworks from quartets

delete2007-02-01
delete32
delete
OA
AI
B
Barbara R. Holland *
G
Glenn Conner
K
Katharina T. Huber
M
Moulton, V.
DOI:10.1080/10635150601167013delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
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.
Keywords:
consensus networks
consensus trees
genome phylogeny
phylogenetic networks
phylogenetic trees
supernetworks
supertrees
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Systematic Biology cover
Systematic Biology
IF:
5.7
Papers:
2.2K
Citations:
1.9W

Organization

No organization information available