Title
Computing galled networks from real data
Abstract
Motivation: Developing methods for computing phylogenetic networks from biological data is an important problem posed by molecular evolution and much work is currently being undertaken in this area. Although promising approaches exist, there are no tools available that biologists could easily and routinely use to compute rooted phylogenetic networks on real datasets containing tens or hundreds of taxa. Biologists are interested in clades, i.e. groups of monophyletic taxa, and these are usually represented by clusters in a rooted phylogenetic tree. The problem of computing an optimal rooted phylogenetic network from a set of clusters, is hard, in general. Indeed, even the problem of just determining whether a given network contains a given cluster is hard. Hence, some researchers have focused on topologically restricted classes of networks, such as galled trees and level-k networks, that are more tractable, but have the practical draw-back that a given set of clusters will usually not possess such a representation. Results: In this article, we argue that galled networks (a generalization of galled trees) provide a good trade-off between level of generality and tractability. Any set of clusters can be represented by some galled network and the question whether a cluster is contained in such a network is easy to solve. Although the computation of an optimal galled network involves successively solving instances of two different NP-complete problems, in practice our algorithm solves this problem exactly on large datasets containing hundreds of taxa and many reticulations in seconds, as illustrated by a dataset containing 279 prokaryotes. Availability: We provide a fast, robust and easy-to-use implementation of this work in version 2.0 of our tree-handling software Dendroscope, freely available from http://www.dendroscope.org. Contact: huson@informatik.uni-tuebingen.de
Year
DOI
Venue
2009
10.1093/bioinformatics/btp217
Bioinformatics
Keywords
Field
DocType
np complete problem,computational biology,molecular evolution,phylogenetic network,phylogeny,biological data
Biological data,Cluster (physics),Data mining,Clade,Phylogenetic tree,Computer science,Theoretical computer science,Software,Bioinformatics,Generality,Phylogenetic network,Computation
Journal
Volume
Issue
ISSN
25
12
1367-4803
Citations 
PageRank 
References 
16
1.01
22
Authors
5
Name
Order
Citations
PageRank
Daniel H. Huson176591.20
Regula Rupp21045.47
Vincent Berry3876.87
Philippe Gambette4779.61
Christophe Paul51479.72