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有根系统发生树和网络的缠结图。

Tanglegrams for rooted phylogenetic trees and networks.

机构信息

Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany.

出版信息

Bioinformatics. 2011 Jul 1;27(13):i248-56. doi: 10.1093/bioinformatics/btr210.

Abstract

MOTIVATION

In systematic biology, one is often faced with the task of comparing different phylogenetic trees, in particular in multi-gene analysis or cospeciation studies. One approach is to use a tanglegram in which two rooted phylogenetic trees are drawn opposite each other, using auxiliary lines to connect matching taxa. There is an increasing interest in using rooted phylogenetic networks to represent evolutionary history, so as to explicitly represent reticulate events, such as horizontal gene transfer, hybridization or reassortment. Thus, the question arises how to define and compute a tanglegram for such networks.

RESULTS

In this article, we present the first formal definition of a tanglegram for rooted phylogenetic networks and present a heuristic approach for computing one, called the NN-tanglegram method. We compare the performance of our method with existing tree tanglegram algorithms and also show a typical application to real biological datasets. For maximum usability, the algorithm does not require that the trees or networks are bifurcating or bicombining, or that they are on identical taxon sets.

AVAILABILITY

The algorithm is implemented in our program Dendroscope 3, which is freely available from www.dendroscope.org.

CONTACT

scornava@informatik.uni-tuebingen.de; huson@informatik.uni-tuebingen.de.

摘要

动机

在系统生物学中,人们经常面临比较不同系统发育树的任务,特别是在多基因分析或共进化研究中。一种方法是使用缠结图,其中将两个有根的系统发育树相对绘制,并使用辅助线连接匹配的分类群。越来越多的人对使用有根系统发育网络来表示进化历史感兴趣,以便明确表示网状事件,如水平基因转移、杂交或重配。因此,出现了如何为这种网络定义和计算缠结图的问题。

结果

在本文中,我们提出了有根系统发育网络缠结图的第一个正式定义,并提出了一种计算它的启发式方法,称为 NN-缠结图方法。我们比较了我们的方法与现有树缠结图算法的性能,还展示了一个典型的应用于真实生物数据集的示例。为了最大限度地提高可用性,该算法不需要树或网络是分支的或双组合的,也不需要它们具有相同的分类群集。

可用性

该算法在我们的程序 Dendroscope 3 中实现,该程序可从 www.dendroscope.org 免费获得。

联系方式

scornava@informatik.uni-tuebingen.dehuson@informatik.uni-tuebingen.de

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abad/3117342/9c6ed884ea9d/btr210f1.jpg

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