Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes

  title={Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes},
  author={Elizabeth Gross and Leo van Iersel and Remie Janssen and Mark Jones and Colby Long and Yukihiro Murakami},
  journal={Journal of Mathematical Biology},
Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and lateral gene transfer. Recently, network-based Markov models of DNA sequence evolution have been introduced along with model-based methods for reconstructing phylogenetic networks. For these methods to be consistent, the network parameter needs to be identifiable from data generated… 

Invariants for level-1 phylogenetic networks under the Cavendar-Farris-Neyman Model

This paper determines all quadratic invariants in the sunlet network ideal which is conjecture generate the full ideal of the Cavendar-Farris-Neyman (CFN) model on level-1 phylogenetic networks.

Distinguishing Level-2 Phylogenetic Networks Using Phylogenetic Invariants

This paper studies the distinguishability of phylogenetic network models associated with level-2 networks using an algebraic approach, namely using discrete Fourier transformation, which generalize earlier work on the distinguishable of level-1 networks.

Statistical learning with phylogenetic network invariants

This work proposes a method of utilizing invariant residuals and support vector machines to infer 4-leaf level-one phylogenetic networks, from which larger networks can be reconstructed.

Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants

This work is the first to define phylogenetic invariants on concordance factors (frequencies of 4-taxon splits in the input gene trees) to identify level-1 phylogenetic networks under the multispecies coalescent model.

Classes of explicit phylogenetic networks and their biological and mathematical significance

A thorough review of several subclasses of rooted phylogenetic networks (characterized by certain structural constraints) introduced in the literature, either to model specific biological phenomena or to enable tractable mathematical and computational analyses.

Identifiability of species network topologies from genomic sequences using the logDet distance

It is shown that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case, and under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.

Identifiability of local and global features of phylogenetic networks from average distances

The “distance split tree” is proposed, which can be constructed from pairwise distances, and it is proved that it is a refinement of the network’s tree of blobs, capturing the tree-like features of the networks, including polytomies from blobs.



On the Identifiability of Phylogenetic Networks under a Pseudolikelihood model

The mathematical version of the identifiability proofs of phylogenetic networks under the pseudolikelihood model in SNaQ is presented, establishing that the ability to detect different hybridization events depends on the number of nodes on the hybridization blob.

Reconstructible Phylogenetic Networks: Do Not Distinguish the Indistinguishable

A novel definition of what constitutes a uniquely reconstructible network is introduced, which is a canonical network that, under mild assumptions, is unique and thus representative of the entire set.

Distinguishing Phylogenetic Networks

Using tools from computational algebraic geometry, it is shown that the semi-directed network topology is generically identifiable for Jukes-Cantor large-cycle network models.

Phylogenetic Networks - Concepts, Algorithms and Applications

This book provides the first interdisciplinary overview of phylogenetic networks, beginning with a concise introduction to both phylogenetic trees and phylogenetic Networks, and presenting the fundamental concepts and results for both rooted and unrooted phylogenetics networks.

Inferring Phylogenetic Networks with Maximum Pseudolikelihood under Incomplete Lineage Sorting

This work presents a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework and applies it to reconstruct the evolutionary relationships among swordtails and platyfishes, which is characterized by widespread hybridizations.

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

NANUQ: a method for inferring species networks from gene trees under the coalescent model

A new algorithm for statistical inference of a level-1 species network under the network multispecies coalescent model is proposed, from data consisting of gene tree topologies, and the theoretical justification for it is provided.

Identifying Species Network Features from Gene Tree Quartets Under the Coalescent Model

  • H. Baños
  • Computer Science
    Bulletin of mathematical biology
  • 2019
It is shown that many topological features of level-1 species networks are identifiable from the distribution of the gene tree quartets under the network multi-species coalescent model, a step toward justifying the inference of such networks.

A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

An efficient, practical algorithm for reconstructing level-1 phylogenetic networks-a type of network slightly more general than a phylogenetic tree-from triplets that is able to construct networks consistent with a high percentage of input triplets, even when theseinput triplets are affected by a low to moderate level of noise.