17,989 research outputs found
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 Note on Encodings of Phylogenetic Networks of Bounded Level
Driven by the need for better models that allow one to shed light into the
question how life's diversity has evolved, phylogenetic networks have now
joined phylogenetic trees in the center of phylogenetics research. Like
phylogenetic trees, such networks canonically induce collections of
phylogenetic trees, clusters, and triplets, respectively. Thus it is not
surprising that many network approaches aim to reconstruct a phylogenetic
network from such collections. Related to the well-studied perfect phylogeny
problem, the following question is of fundamental importance in this context:
When does one of the above collections encode (i.e. uniquely describe) the
network that induces it? In this note, we present a complete answer to this
question for the special case of a level-1 (phylogenetic) network by
characterizing those level-1 networks for which an encoding in terms of one (or
equivalently all) of the above collections exists. Given that this type of
network forms the first layer of the rich hierarchy of level-k networks, k a
non-negative integer, it is natural to wonder whether our arguments could be
extended to members of that hierarchy for higher values for k. By giving
examples, we show that this is not the case
How much information is needed to infer reticulate evolutionary histories?
Phylogenetic networks are a generalization of evolutionary trees and are an important tool for analyzing reticulate evolutionary histories. Recently, there has been great interest in developing new methods to construct rooted phylogenetic networks, that is, networks whose internal vertices correspond to hypothetical ancestors, whose leaves correspond to sampled taxa, and in which vertices with more than one parent correspond to taxa formed by reticulate evolutionary events such as recombination or hybridization. Several methods for constructing evolutionary trees use the strategy of building up a tree from simpler building blocks (such as triplets or clusters), and so it is natural to look for ways to construct networks from smaller networks. In this article, we shall demonstrate a fundamental issue with this approach. Namely, we show that even if we are given all of the subnetworks induced on all proper subsets of the leaves of some rooted phylogenetic network, we still do not have all of the information required to completely determine that network. This implies that even if all of the building blocks for some reticulate evolutionary history were to be taken as the input for any given network building method, the method might still output an incorrect history. We also discuss some potential consequences of this result for constructing phylogenetic networks
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
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
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
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