Literature DB >> 34739608

Discrete coalescent trees.

Lena Collienne1, Kieran Elmes1, Mareike Fischer2, David Bryant3, Alex Gavryushkin4,5.   

Abstract

In many phylogenetic applications, such as cancer and virus evolution, time trees, evolutionary histories where speciation events are timed, are inferred. Of particular interest are clock-like trees, where all leaves are sampled at the same time and have equal distance to the root. One popular approach to model clock-like trees is coalescent theory, which is used in various tree inference software packages. Methodologically, phylogenetic inference methods require a tree space over which the inference is performed, and the geometry of this space plays an important role in statistical and computational aspects of tree inference algorithms. It has recently been shown that coalescent tree spaces possess a unique geometry, different from that of classical phylogenetic tree spaces. Here we introduce and study a space of discrete coalescent trees. They assume that time is discrete, which is natural in many computational applications. This tree space is a generalisation of the previously studied ranked nearest neighbour interchange space, and is built upon tree-rearrangement operations. We generalise existing results about ranked trees, including an algorithm for computing distances in polynomial time, and in particular provide new results for both the space of discrete coalescent trees and the space of ranked trees. We establish several geometrical properties of these spaces and show how these properties impact various algorithms used in phylogenetic analyses. Our tree space is a discretisation of a previously introduced time tree space, called t-space, and hence our results can be used to approximate solutions to various open problems in t-space.
© 2021. The Author(s).

Entities:  

Keywords:  Algorithms; Coalescent trees; Metric geometry; Phylogenetics; Ranked tree; Time trees; Tree distance; Tree space

Mesh:

Year:  2021        PMID: 34739608      PMCID: PMC8571255          DOI: 10.1007/s00285-021-01685-0

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  17 in total

1.  Polyhedral geometry of phylogenetic rogue taxa.

Authors:  María Angélica Cueto; Frederick A Matsen
Journal:  Bull Math Biol       Date:  2010-07-17       Impact factor: 1.758

2.  Advances in Time Estimation Methods for Molecular Data.

Authors:  Sudhir Kumar; S Blair Hedges
Journal:  Mol Biol Evol       Date:  2016-02-16       Impact factor: 16.240

3.  Hybrids in real time.

Authors:  Mihaela Baroni; Charles Semple; Mike Steel
Journal:  Syst Biol       Date:  2006-02       Impact factor: 15.683

4.  MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

Authors:  Koichiro Tamura; Daniel Peterson; Nicholas Peterson; Glen Stecher; Masatoshi Nei; Sudhir Kumar
Journal:  Mol Biol Evol       Date:  2011-05-04       Impact factor: 16.240

5.  Maximum likelihood estimation of population growth rates based on the coalescent.

Authors:  M K Kuhner; J Yamato; J Felsenstein
Journal:  Genetics       Date:  1998-05       Impact factor: 4.562

Review 6.  Coalescent genealogy samplers: windows into population history.

Authors:  Mary K Kuhner
Journal:  Trends Ecol Evol       Date:  2008-12-26       Impact factor: 17.712

7.  The combinatorics of discrete time-trees: theory and open problems.

Authors:  Alex Gavryushkin; Chris Whidden; Frederick A Matsen
Journal:  J Math Biol       Date:  2017-07-29       Impact factor: 2.259

8.  Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10.

Authors:  Marc A Suchard; Philippe Lemey; Guy Baele; Daniel L Ayres; Alexei J Drummond; Andrew Rambaut
Journal:  Virus Evol       Date:  2018-06-08

9.  CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples.

Authors:  David Posada
Journal:  Mol Biol Evol       Date:  2020-05-01       Impact factor: 16.240

10.  BEAST 2: a software platform for Bayesian evolutionary analysis.

Authors:  Remco Bouckaert; Joseph Heled; Denise Kühnert; Tim Vaughan; Chieh-Hsi Wu; Dong Xie; Marc A Suchard; Andrew Rambaut; Alexei J Drummond
Journal:  PLoS Comput Biol       Date:  2014-04-10       Impact factor: 4.475

View more
  1 in total

1.  Network representation and analysis of energy coupling mechanisms in cellular metabolism by a graph-theoretical approach.

Authors:  Sunil Nath
Journal:  Theory Biosci       Date:  2022-05-02       Impact factor: 1.315

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.