Literature DB >> 29548737

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

Andrew Francis1, Vincent Moulton2.   

Abstract

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2018        PMID: 29548737     DOI: 10.1016/j.jtbi.2018.03.011

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  2 in total

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

Authors:  Elizabeth Gross; Leo van Iersel; Remie Janssen; Mark Jones; Colby Long; Yukihiro Murakami
Journal:  J Math Biol       Date:  2021-09-04       Impact factor: 2.259

2.  A polynomial invariant for a new class of phylogenetic networks.

Authors:  Joan Carles Pons; Tomás M Coronado; Michael Hendriksen; Andrew Francis
Journal:  PLoS One       Date:  2022-05-20       Impact factor: 3.752

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.