A Distance-Based Method for Inferring Phylogenetic Networks in the Presence of Incomplete Lineage Sorting

Yun, Yu; Nakhleh, Luay

doi:10.1007/978-3-319-19048-8_32

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…PhyloNet also includes a method for distance-based inference of phylogenetic networks (Yu and Nakhleh, 2015a), as well as a Gibbs sampling method for estimating the parameters of a given phylogenetic network (Yu et al, 2016).…”

Section: Other Featuresmentioning

confidence: 99%

Inferring Phylogenetic Networks Using PhyloNet

Wen¹,

Yu²,

Zhu³

et al. 2018

Systematic Biology

Self Cite

259

197

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PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

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Section: Other Featuresmentioning

confidence: 99%

Inferring Phylogenetic Networks Using PhyloNet

Wen¹,

Yu²,

Zhu³

et al. 2018

Systematic Biology

Self Cite

259

197

View full text Add to dashboard Cite

show abstract

“…In recent years, some efforts have been made to address this issue, but they all focused on limited special cases of phylogenetic networks [ 15 - 20 ]. More recently, methods have been developed for general phylogenetic networks, including maximum parsimony [ 21 ], maximum likelihood [ 22 - 24 ] and distance-based methods [ 25 ]. Of these, maximum likelihood produces the most accurate results and allows for estimating, in addition to the network topology, branch lengths and other parameters.…”

Section: Introductionmentioning

confidence: 99%

A maximum pseudo-likelihood approach for phylogenetic networks

2015

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BackgroundSeveral phylogenomic analyses have recently demonstrated the need to account simultaneously for incomplete lineage sorting (ILS) and hybridization when inferring a species phylogeny. A maximum likelihood approach was introduced recently for inferring species phylogenies in the presence of both processes, and showed very good results. However, computing the likelihood of a model in this case is computationally infeasible except for very small data sets.ResultsInspired by recent work on the pseudo-likelihood of species trees based on rooted triples, we introduce the pseudo-likelihood of a phylogenetic network, which, when combined with a search heuristic, provides a statistical method for phylogenetic network inference in the presence of ILS. Unlike trees, networks are not always uniquely encoded by a set of rooted triples. Therefore, even when given sufficient data, the method might converge to a network that is equivalent under rooted triples to the true one, but not the true one itself. The method is computationally efficient and has produced very good results on the data sets we analyzed. The method is implemented in PhyloNet, which is publicly available in open source.ConclusionsMaximum pseudo-likelihood allows for inferring species phylogenies in the presence of hybridization and ILS, while scaling to much larger data sets than is currently feasible under full maximum likelihood. The nonuniqueness of phylogenetic networks encoded by a system of rooted triples notwithstanding, the proposed method infers the correct network under certain scenarios, and provides candidates for further exploration under other criteria and/or data in other scenarios.

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“…Other assumptions would also be needed on population sizes, shared or not across lineages. The recent approach in [41] should scale well to many taxa, but makes these strong assumptions because it requires accurate distances obtained from branch lengths in gene trees. On the contrary, our approach should be robust to rate variation across genes and across lineages, and does not require any assumption on population sizes.…”

Section: Discussionmentioning

confidence: 99%

Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting

Solís-Lemus¹,

Ané²

2015

Preprint

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Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development.The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes (Xiphophorus: Poeciliidae), which is characterized by widespread hybridizations.

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A Distance-Based Method for Inferring Phylogenetic Networks in the Presence of Incomplete Lineage Sorting

Cited by 8 publications

References 37 publications

Inferring Phylogenetic Networks Using PhyloNet

Inferring Phylogenetic Networks Using PhyloNet

A maximum pseudo-likelihood approach for phylogenetic networks

Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting

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