Normalising phylogenetic networks

Methods in Ecology and Evolution

The fields of phylogenetic tree and network inference have dramatically advanced in the past decade, but independently with few attempts to bridge them. Here we provide a framework, implemented in the PHANGORN library in R, to transfer information between trees and networks. This includes: (i) identifying and labelling equivalent tree branches and network edges, (ii) transferring tree branch support to network edges, and (iii) mapping bipartition support from a sample of trees (e.g. from boot-strapping or Bayesian inference) onto network edges. The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.

Bulletin of Mathematical Biology, 2022

As phylogenetic networks grow increasingly complicated, systematic methods for simplifying them to reveal properties will become more useful. This paper considers how to modify acyclic phylogenetic networks into other acyclic networks by contracting specific arcs that include a set D . The networks need not be binary, so vertices in the networks may have more than two parents and/or more than two children. In general, in order to make the resulting network acyclic, additional arcs not in D must also be contracted. This paper shows how to choose D so that the resulting acyclic network is “pre-normal”. As a result, removal of all redundant arcs yields a normal network. The set D can be selected based only on the geometry of the network, giving a well-defined normal phylogenetic network depending only on the given network. There are CSD maps relating most of the networks. The resulting network can be visualized as a “wired lift” in the original network, which appears as the original ne...

2010

Since Darwin, species trees have been used as a simplified description of the relationships which summarize the complicated network N of reality. Recent evidence of hybridization and lateral gene transfer, however, suggest that there are situations where trees are inadequate. Consequently it is important to determine properties that characterize networks closely related to N and possibly more complicated than trees but lacking the full complexity of N. A connected surjective digraph map (CSD) is a map f from one network N to another network M such that every arc is either collapsed to a single vertex or is taken to an arc, such that f is surjective, and such that the inverse image of a vertex is always connected. CSD maps are shown to behave well under composition. It is proved that if there is a CSD map from N to M , then there is a way to lift an undirected version of M into N , often with added resolution. A CSD map from N to M puts strong constraints on N. In general, it may be useful to study classes of networks such that, for any N , there exists a CSD map from N to some standard member of that class.

Bioinformatics, 2013

Motivation: The evolutionary history of species is traditionally represented with a rooted phylogenetic tree. Each tree comprises a set of clusters, i.e. subsets of the species that are descended from a common ancestor. When rooted phylogenetic trees are built from several different datasets (e.g. from different genes), the clusters are often conflicting. These conflicting clusters cannot be expressed as a simple phylogenetic tree; however, they can be expressed in a phylogenetic network. Phylogenetic networks are a generalization of phylogenetic trees that can account for processes such as hybridization, horizontal gene transfer and recombination, which are difficult to represent in standard tree-like models of evolutionary histories. There is currently a large body of research aimed at developing appropriate methods for constructing phylogenetic networks from cluster sets. The CASS algorithm can construct a much simpler network than other available methods, but is extremely slow for large datasets or for datasets that need lots of reticulate nodes. The networks constructed by CASS are also greatly dependent on the order of input data, i.e. it generally derives different phylogenetic networks for the same dataset when different input orders are used. Results: In this study, we introduce an improved CASS algorithm, LNETWORK, which can construct a phylogenetic network for a given set of clusters. We show that LNETWORK is significantly faster than CASS and effectively weakens the influence of input data order. Moreover, we show that LNETWORK can construct a much simpler network than most of the other available methods. Availability: LNETWORK has been built as a Java software package and

Bioinformatics, 2011

In systematic biology, one is often faced with the task of comparing different phylogenetic trees, in particular in multigene 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.

2012

The problem of constructing an optimal rooted phylogenetic‎ ‎network from a set of rooted triplets is NP-hard. ‎In this paper‎, ‎we present a novel method called NCH‎, ‎which tries to construct a rooted phylogenetic network with the minimum number‎‎of reticulation nodes from an arbitrary set of rooted triplets based on the concept of the height function of a tree and a network. We report the performance of this method on simulated data. Keywords— Rooted phylogenetic network, Triplet, Density, Consistency, Height function, Reticulation node

Systematic Biology, 2012

Dendroscope 3 is a new program for working with rooted phylogenetic trees and networks. It provides a number of methods for drawing and comparing rooted phylogenetic networks, and for computing them from rooted trees. The program can be used interactively or in command-line mode. The program is written in Java, use of the software is free, and installers for all 3 major operating systems can be downloaded from www.dendroscope.org. [Phylogenetic trees; phylogenetic networks; software.] SYSTEMATIC BIOLOGY VOL. 61 FIGURE 2. A minimum hybridization network computed by Dendroscope 3 for the cpDNA and ITS trees that are reported in Figure 1 of Pirie et al. (2009). It has 12 reticulations and is one of 486 networks calculated by the program. by guest on July 6, 2016 http://sysbio.oxfordjournals.org/ Downloaded from

2023

This paper studies the relationship between undirected (unrooted) and directed (rooted) phylogenetic networks. We describe a polynomial-time algorithm for deciding whether an undirected nonbinary phylogenetic network, given the locations of the root and reticulation vertices, can be oriented as a directed nonbinary phylogenetic network. Moreover, we characterize when this is possible and show that, in such instances, the resulting directed nonbinary phylogenetic network is unique. In addition, without being given the location of the root and the reticulation vertices, we describe an algorithm for deciding whether an undirected binary phylogenetic network N can be oriented as a directed binary phylogenetic network of a certain class. The algorithm is fixed-parameter tractable (FPT) when the parameter is the level of N and is applicable to classes of directed phylogenetic networks that satisfy certain conditions. As an example, we show that the well-studied class of binary tree-child networks satisfies these conditions.

International Journal for Parasitology, 2005

Phylogenetic analysis has changed greatly in the past decade, including the more widespread appreciation of the idea that evolutionary histories are not always tree-like, and may, thus, be best represented as reticulated networks rather than as strictly dichotomous trees. Reconstructing such histories in the absence of a bifurcating speciation process is even more difficult than the usual procedure, and a range of alternative strategies have been developed. There seem to be two basic uses for a network model of evolution: the display of real but unobservable evolutionary events (i.e. a hypothesis of the true phylogenetic history), and the display of character conflict within the data itself (i.e. a summary of the data). These two general approaches are briefly reviewed here, and the strengths and weaknesses of the different implementations are compared and contrasted. Each network methodology seems to have limitations in terms of how it responds to increasing complexity (e.g. conflict) in the data, and therefore each is likely to be more appropriate for one of the two uses than for the other. Several examples using parasitological data sets illustrate the uses of networks within the context of population biology.

PLoS ONE, 2014

We present TripNet, a method for constructing phylogenetic networks from triplets. We will present the motivations behind our approach and its theoretical and empirical justification. To demonstrate the accuracy and efficiency of TripNet, we performed two simulations and also applied the method to five published data sets: Kreitman's data, a set of triplets from real yeast data obtained from the Fungal Biodiversity Center in Utrecht, a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, and nrDNA ITS and cpDNA JSA sequence data of New Zealand alpine buttercups of Ranunculus sect. Pseudadonis. Finally, we compare our results with those already obtained by other authors using alternative methods. TripNet, data sets, and supplementary files are freely available for download at (www.bioinf.cs.ipm.ir/softwares/tripnet).