235 research outputs found
A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks
Recently much attention has been devoted to the construction of phylogenetic
networks which generalize phylogenetic trees in order to accommodate complex
evolutionary processes. Here we present an efficient, practical algorithm for
reconstructing level-1 phylogenetic networks - a type of network slightly more
general than a phylogenetic tree - from triplets. Our algorithm has been made
publicly available as the program LEV1ATHAN. It combines ideas from several
known theoretical algorithms for phylogenetic tree and network reconstruction
with two novel subroutines. Namely, an exponential-time exact and a greedy
algorithm both of which are of independent theoretical interest. Most
importantly, LEV1ATHAN runs in polynomial time and always constructs a level-1
network. If the data is consistent with a phylogenetic tree, then the algorithm
constructs such a tree. Moreover, if the input triplet set is dense and, in
addition, is fully consistent with some level-1 network, it will find such a
network. The potential of LEV1ATHAN is explored by means of an extensive
simulation study and a biological data set. One of our conclusions is that
LEV1ATHAN is able to construct networks consistent with a high percentage of
input triplets, even when these input triplets are affected by a low to
moderate level of noise
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
Trinets encode tree-child and level-2 phylogenetic networks
Phylogenetic networks generalize evolutionary trees, and are commonly used to
represent evolutionary histories of species that undergo reticulate
evolutionary processes such as hybridization, recombination and lateral gene
transfer. Recently, there has been great interest in trying to develop methods
to construct rooted phylogenetic networks from triplets, that is rooted trees
on three species. However, although triplets determine or encode rooted
phylogenetic trees, they do not in general encode rooted phylogenetic networks,
which is a potential issue for any such method. Motivated by this fact, Huber
and Moulton recently introduced trinets as a natural extension of rooted
triplets to networks. In particular, they showed that level-1 phylogenetic
networks are encoded by their trinets, and also conjectured that all
"recoverable" rooted phylogenetic networks are encoded by their trinets. Here
we prove that recoverable binary level-2 networks and binary tree-child
networks are also encoded by their trinets. To do this we prove two
decomposition theorems based on trinets which hold for all recoverable binary
rooted phylogenetic networks. Our results provide some additional evidence in
support of the conjecture that trinets encode all recoverable rooted
phylogenetic networks, and could also lead to new approaches to construct
phylogenetic networks from trinets
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
On the challenge of reconstructing level-1 phylogenetic networks from triplets and clusters
Phylogenetic networks have gained prominence over the years due to their ability to represent complex non-treelike evolutionary events such as recombination or hybridization. Popular combinatorial objects used to construct them are triplet systems and cluster systems, the motivation being that any network induces a triplet system and a softwired cluster system . Since in real-world studies it cannot be guaranteed that all triplets/softwired clusters induced by a network are available, it is of particular interest to understand whether subsets of or allow one to uniquely reconstruct the underlying network . Here we show that even within the highly restricted yet biologically interesting space of level-1 phylogenetic networks it is not always possible to uniquely reconstruct a level-1 network \kelk{,} even when all triplets in or all clusters in are available. On the positive side, we introduce a reasonably large subclass of level-1 networks the members of which are uniquely determined by their induced triplet/softwired cluster systems. Along the way, we also establish various enumerative results, both positive and negative, including results which show that certain special subclasses of level-1 networks can be uniquely reconstructed from proper subsets of and . We anticipate these results to be of use in the design of algorithms for phylogenetic network inference
Randomized approximation algorithms : facility location, phylogenetic networks, Nash equilibria
Despite a great effort, researchers are unable to find efficient algorithms for a number of natural computational problems. Typically, it is possible to emphasize the hardness of such problems by proving that they are at least as hard as a number of other problems. In the language of computational complexity it means proving that the problem is complete for a certain class of problems. For optimization problems, we may consider to relax the requirement of the outcome to be optimal and accept an approximate (i.e., close to optimal) solution. For many of the problems that are hard to solve optimally, it is actually possible to efficiently find close to optimal solutions. In this thesis, we study algorithms for computing such approximate solutions
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