The Comparison of Tree-Sibling Time Consistent Phylogenetic Networks Is Graph Isomorphism-Complete

Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel

doi:10.1155/2014/254279

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…For example, while testing if two trees are isomorphic can be done in polynomial time, the problem is NP-hard for phylogenetic networks in general. This is why various dissimilarity measures for restricted classes of phylogenetic networks have been devised [22,3,4,2,11,5,36]. While PhyloNet has utilities to compare phylogenetic networks based on their constituent trees, tripartitions, and clusters, it also has a function that computes the distance measure of [22].…”

Section: Comparing and Summarizing Networkmentioning

confidence: 99%

Practical Aspects of Phylogenetic Network Analysis Using PhyloNet

Cao

Liu

Ogilvie

et al. 2019

Preprint

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Phylogenetic networks extend trees to enable simultaneous modeling of both vertical and horizontal evolutionary processes. PhyloNet is a software package that has been under constant development for over 10 years and includes a wide array of functionalities for inferring and analyzing phylogenetic networks. These functionalities differ in terms of the input data they require, the criteria and models they employ, and the types of information they allow to infer about the networks beyond their topologies. Furthermore, PhyloNet includes functionalities for simulating synthetic data on phylogenetic networks, quantifying the topological differences between phylogenetic networks, and evaluating evolutionary hypotheses given in the form of phylogenetic networks.In this paper, we use a simulated data set to illustrate the use of several of PhyloNet's functionalities and make recommendations on how to analyze data sets and interpret the results when using these functionalities. All inference methods that we illustrate are incomplete lineage sorting (ILS) aware; that is, they account for the potential of ILS in the data while inferring the phylogenetic network. While the models do not include gene duplication and loss, we discuss how the methods can be used to analyze data in the presence of polyploidy.The concept of species is irrelevant for the computational analyses enabled by PhyloNet in that species-individuals mappings are user-defined. Consequently, none of the functionalities in PhyloNet deals with the task of species delimitation. In this sense, the data being analyzed could come from different individuals within a single species, in which case population structure along with potential gene flow is inferred (assuming the data has sufficient signal), or from different individuals sampled from different species, in which case the species phylogeny is being inferred. * nakhleh@rice.edu P(D|Ψ) = -1443 P(D|Ψ) = -1439 P(D|Ψ) = -1454 P(D|Ψ) = -1435 P(D|Ψ) = -1435 P(D|Ψ) = -1439 P(D|Ψ) = -1435 P(D|Ψ) = -1439 P(D|Ψ) = -1434 P(D|Ψ) = -1443 P(D|Ψ) = -1439 P(D|Ψ) = -1454 P(D|Ψ) = -14547 posterior distribution over all the possible trees. As Fig 3(b) shows, the random walk visits the different trees, but "circulates" among three topologies more than the other 12 topologies. The posteriors of the trees visited are plotted as in Fig. 3(c), and then the marginal probability distribution on tree topologies is summarized from the samples, as in Fig. 3(d). While one could return the tree (D,((A,E),(B,C))) as the one having the highest marginal probability (the yellow bar is the highest in Fig. 3(d)), the power of Bayesian analysis is that the totality of the results shown in the figure provide more information than just the optimal point. For example, the results in Fig. 3(d) show that the confidence level does not exceed 40% for any of the possible 15 trees. Furthermore, it shows that two trees have comparable probabilities, while a third one is close to them. Back to network inference, any search strategy begins with one or more s...

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Section: Comparing and Summarizing Networkmentioning

confidence: 99%

Practical Aspects of Phylogenetic Network Analysis Using PhyloNet

Cao

Liu

Ogilvie

et al. 2019

Preprint

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“…The networks that PhyNEST estimates are time-consistent tree-child (TCTC) level-1 networks as these networks make biological sense and are computationally tractable (see Kong et al (2022) for a formal definition). The biological motivation behind time-consistency is that two taxa involved in a hybridization event must have temporally coexisted in order to interbreed (Cardona et al, 2014). In theory, the two incoming edges should have an edge length equal to zero since hybridization is an instantaneous process.…”

Section: Modelmentioning

confidence: 99%

Inference of Phylogenetic Networks from Sequence Data using Composite Likelihood

Kong

Swofford

Kubatko

2022

Preprint

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While phylogenies have been essential in understanding how species evolve, they do not adequately describe some evolutionary processes. For instance, hybridization, a common phenomenon where interbreeding between two species leads to formation of a new species, must be depicted by a phylogenetic network, a structure that modifies a phylogeny by allowing two branches to merge into one, resulting in reticulation. However, existing methods for estimating networks are computationally expensive as the dataset size and/or topological complexity increase. The lack of methods for scalable inference hampers phylogenetic networks from being widely used in practice, despite accumulating evidence that hybridization occurs frequently in nature. Here, we propose a novel method, PhyNEST (Phylogenetic Network Estimation using SiTe patterns), that estimates phylogenetic networks directly from sequence data. PhyNEST achieves computational efficiency by using composite likelihood as well as accuracy by using the full genomic data to incorporate all sources of variability, rather than first summarizing the data by estimating a set of gene trees, as is required by most of the existing methods. To efficiently search network space, we implement both hill-climbing and simulated annealing algorithms. Simulation studies show that PhyNEST can accurately estimate parameters given the true network topology and that it has comparable accuracy to two popular methods that use composite likelihood and a set of gene trees as input, implemented in SNaQ and PhyloNet. For datasets with a large number of loci, PhyNEST is more efficient than SNaQ and PhyloNet when considering the time required for gene tree estimation. We applied PhyNEST to reconstruct the evolutionary relationships among Heliconius butterflies and Papionini primates, characterized by hybrid speciation and widespread introgression, respectively. PhyNEST is implemented in an open-source Julia package and publicly available at https://github.com/sungsik-kong/PhyNEST.jl

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“…Several classes of phylogenetic networks have a unique, up to isomorphism, representation as a multiset of clusters, including binary galled trees [12], tree-child time-consistent phylogenetic networks [11,16,17], and semi-binary tree-sibling time-consistent phylogenetic networks [14]. Notice that more general classes of phylogenetic networks do not have such a unique representation [13].…”

Section: Phylogenetic Networkmentioning

confidence: 99%