23 research outputs found
Computing galled networks from real data
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
Algorithms for Visualizing Phylogenetic Networks
We study the problem of visualizing phylogenetic networks, which are
extensions of the Tree of Life in biology. We use a space filling visualization
method, called DAGmaps, in order to obtain clear visualizations using limited
space. In this paper, we restrict our attention to galled trees and galled
networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
A simple fixed parameter tractable algorithm for computing the hybridization number of two (not necessarily binary) trees
Here we present a new fixed parameter tractable algorithm to compute the
hybridization number r of two rooted, not necessarily binary phylogenetic trees
on taxon set X in time (6^r.r!).poly(n)$, where n=|X|. The novelty of this
approach is its use of terminals, which are maximal elements of a natural
partial order on X, and several insights from the softwired clusters
literature. This yields a surprisingly simple and practical bounded-search
algorithm and offers an alternative perspective on the underlying combinatorial
structure of the hybridization number problem
The tree-child network problem and the shortest common supersequences for permutations are NP-hard
Reconstructing phylogenetic networks presents a significant and complex
challenge within the fields of phylogenetics and genome evolution. One strategy
for reconstruction of phylogenetic networks is to solve the phylogenetic
network problem, which involves inferring phylogenetic trees first and
subsequently computing the smallest phylogenetic network that displays all the
trees. This approach capitalizes on exceptional tools available for inferring
phylogenetic trees from biomolecular sequences. Since the vast space of
phylogenetic networks poses difficulties in obtaining comprehensive sampling,
the researchers switch their attention to inferring tree-child networks from
multiple phylogenetic trees, where in a tree-child network each non-leaf node
must have at least one child that is a tree node (i.e. indegree-one node). We
prove that the tree-child network problem for multiple trees remains NP-hard by
a reduction from the shortest common supersequnece problem for permuations and
proving that the latter is NP-hard.Comment: 3 figures and 11 page
When two trees go to war
Rooted phylogenetic networks are often constructed by combining trees,
clusters, triplets or characters into a single network that in some
well-defined sense simultaneously represents them all. We review these four
models and investigate how they are related. In general, the model chosen
influences the minimum number of reticulation events required. However, when
one obtains the input data from two binary trees, we show that the minimum
number of reticulations is independent of the model. The number of
reticulations necessary to represent the trees, triplets, clusters (in the
softwired sense) and characters (with unrestricted multiple crossover
recombination) are all equal. Furthermore, we show that these results also hold
when not the number of reticulations but the level of the constructed network
is minimised. We use these unification results to settle several complexity
questions that have been open in the field for some time. We also give explicit
examples to show that already for data obtained from three binary trees the
models begin to diverge
Phylogenetic Networks Do not Need to Be Complex: Using Fewer Reticulations to Represent Conflicting Clusters
Phylogenetic trees are widely used to display estimates of how groups of
species evolved. Each phylogenetic tree can be seen as a collection of
clusters, subgroups of the species that evolved from a common ancestor. When
phylogenetic trees are obtained for several data sets (e.g. for different
genes), then their clusters are often contradicting. Consequently, the set of
all clusters of such a data set cannot be combined into a single phylogenetic
tree. Phylogenetic networks are a generalization of phylogenetic trees that can
be used to display more complex evolutionary histories, including reticulate
events such as hybridizations, recombinations and horizontal gene transfers.
Here we present the new CASS algorithm that can combine any set of clusters
into a phylogenetic network. We show that the networks constructed by CASS are
usually simpler than networks constructed by other available methods. Moreover,
we show that CASS is guaranteed to produce a network with at most two
reticulations per biconnected component, whenever such a network exists. We
have implemented CASS and integrated it in the freely available Dendroscope
software
A Survey of Combinatorial Methods for Phylogenetic Networks
The evolutionary history of a set of species is usually described by a rooted phylogenetic tree. Although it is generally undisputed that bifurcating speciation events and descent with modifications are major forces of evolution, there is a growing belief that reticulate events also have a role to play. Phylogenetic networks provide an alternative to phylogenetic trees and may be more suitable for data sets where evolution involves significant amounts of reticulate events, such as hybridization, horizontal gene transfer, or recombination. In this article, we give an introduction to the topic of phylogenetic networks, very briefly describing the fundamental concepts and summarizing some of the most important combinatorial methods that are available for their computation