On Tree-Based Phylogenetic Networks

Zhang, Louxin

doi:10.1089/cmb.2015.0228

Cited by 64 publications

(98 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each integer in [0, 5], four networks in the data set have that number of reticulation nodes. Two of the 24 networks are not tree-based [25].…”

Section: Resultsmentioning

confidence: 99%

Empirical Performance of Tree-based Inference of Phylogenetic Networks

Cao¹,

Nakhleh²

2019

Preprint

View full text Add to dashboard Cite

Phylogenetic networks extend the phylogenetic tree structure and allow for modeling vertical and horizontal evolution in a single framework. Statistical inference of phylogenetic networks is prohibitive and currently limited to small networks. An approach that could significantly improve phylogenetic network space exploration is based on first inferring an evolutionary tree of the species under consideration, and then augmenting the tree into a network by adding a set of "horizontal" edges to better fit the data.In this paper, we study the performance of such an approach on networks generated under a birth-hybridization model and explore its feasibility as an alternative to approaches that search the phylogenetic network space directly (without relying on a fixed underlying tree). We find that the concatenation method does poorly at obtaining a "backbone" tree that could be augmented into the correct network, whereas the popular species tree inference method ASTRAL does significantly better at such a task. We then evaluated the tree-tonetwork augmentation phase under the minimizing deep coalescence and pseudo-likelihood criteria. We find that even though this is a much faster approach than the direct search of the network space, * the accuracy is much poorer, even when the backbone tree is a good starting tree.Our results show that tree-based inference of phylogenetic networks could yield very poor results. As exploration of the network space directly in search of maximum likelihood estimates or a representative sample of the posterior is very expensive, significant improvements to the computational complexity of phylogenetic network inference are imperative if analyses of large data sets are to be performed. We show that a recently developed divide-and-conquer approach significantly outperforms tree-based inference in terms of accuracy, albeit still at a higher computational cost.

show abstract

“…For each integer in [0, 5], four networks in the data set have that number of reticulation nodes. Two of the 24 networks are not tree-based [25].…”

Section: Resultsmentioning

confidence: 99%

Empirical Performance of Tree-based Inference of Phylogenetic Networks

Cao¹,

Nakhleh²

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Let T be the set of tree vertices in N that are parents of a reticulation and let R be the set of reticulations in N . Zhang [12] defined the following bipartite graph to characterise tree-based networks. Let Z N be the graph with vertex set T ∪ R and edge set {{t, r} : t ∈ T , r ∈ R, and (t, r) is an edge in N }.…”

Section: Previous Resultsmentioning

confidence: 99%

“…In particular, Zhang [12] established the following characterisations. The proofs of our main results make use of these characterisations.…”

Section: Previous Resultsmentioning

confidence: 99%

See 1 more Smart Citation

New characterisations of tree-based networks and proximity measures

Francis

Semple

Steel

2018

Advances in Applied Mathematics

View full text Add to dashboard Cite

Phylogenetic networks are a type of directed acyclic graph that represent how a set X of present-day species are descended from a common ancestor by processes of speciation and reticulate evolution. In the absence of reticulate evolution, such networks are simply phylogenetic (evolutionary) trees. Moreover, phylogenetic networks that are not trees can sometimes be represented as phylogenetic trees with additional directed edges placed between their edges. Such networks are called tree-based, and the class of phylogenetic networks that are tree-based has recently been characterised. In this paper, we establish a number of new characterisations of tree-based networks in terms of path partitions and antichains (in the spirit of Dilworth's theorem), as well as via matchings in a bipartite graph. We also show that a temporal network is treebased if and only if it satisfies an antichain-to-leaf condition. In the second part of the paper, we define three indices that measure the extent to which an arbitrary phylogenetic network deviates from being tree-based. We describe how * Corresponding author.E-mail addresses: a.francis@westernsydney.edu.au (A. Francis), charles.semple@canterbury.ac.nz (C. Semple), mike.steel@canterbury.ac.nz (M. Steel).

show abstract

“…In a second paper, Didelot et al (2010) present a different approximation to the coalescent with gene conversion which retains the ARG but assumes that the ARG has the form of a tree-based network (Zhang 2015) with the clonal frame taking on the role of the base tree. While acknowledging that their model could be applied to jointly infer the clonal frame and the conversions, the algorithm they present is limited to performing inference of the gene conversion ARG given a separatelyinferred clonal frame.…”

mentioning

confidence: 99%

Bayesian inference of ancestral recombination graphs for bacterial populations

Vaughan

Welch

Drummond

et al. 2016

Preprint

View full text Add to dashboard Cite

Homologous recombination is a central feature of bacterial evolution, yet confounds traditional phylogenetic methods. While a number of methods specific to bacterial evolution have been developed, none of these permit joint inference of a bacterial recombination graph and associated parameters. In this paper, we present a new method which addresses this shortcoming. Our method uses a novel Markov chain Monte Carlo algorithm to perform phylogenetic inference under the ClonalOrigin model of Didelot et al. (Genetics, 2010). We demonstrate the utility of our method by applying it to rMLST data sequenced from pathogenic and non-pathogenic Escherichia coli serotype O157 and O26 isolates collected in rural New Zealand. The method is implemented as an open source BEAST 2 package, Bacter, which is available via the project web page at http://tgvaughan.github.io/bacter.

show abstract

On Tree-Based Phylogenetic Networks

Cited by 64 publications

References 24 publications

Empirical Performance of Tree-based Inference of Phylogenetic Networks

Empirical Performance of Tree-based Inference of Phylogenetic Networks

New characterisations of tree-based networks and proximity measures

Bayesian inference of ancestral recombination graphs for bacterial populations

Contact Info

Product

Resources

About