Literature DB >> 35552538

Enumeration of binary trees compatible with a perfect phylogeny.

Julia A Palacios1,2, Anand Bhaskar3, Filippo Disanto4, Noah A Rosenberg5.   

Abstract

Evolutionary models used for describing molecular sequence variation suppose that at a non-recombining genomic segment, sequences share ancestry that can be represented as a genealogy-a rooted, binary, timed tree, with tips corresponding to individual sequences. Under the infinitely-many-sites mutation model, mutations are randomly superimposed along the branches of the genealogy, so that every mutation occurs at a chromosomal site that has not previously mutated; if a mutation occurs at an interior branch, then all individuals descending from that branch carry the mutation. The implication is that observed patterns of molecular variation from this model impose combinatorial constraints on the hidden state space of genealogies. In particular, observed molecular variation can be represented in the form of a perfect phylogeny, a tree structure that fully encodes the mutational differences among sequences. For a sample of n sequences, a perfect phylogeny might not possess n distinct leaves, and hence might be compatible with many possible binary tree structures that could describe the evolutionary relationships among the n sequences. Here, we investigate enumerative properties of the set of binary ranked and unranked tree shapes that are compatible with a perfect phylogeny, and hence, the binary ranked and unranked tree shapes conditioned on an observed pattern of mutations under the infinitely-many-sites mutation model. We provide a recursive enumeration of these shapes. We consider both perfect phylogenies that can be represented as binary and those that are multifurcating. The results have implications for computational aspects of the statistical inference of evolutionary parameters that underlie sets of molecular sequences.
© 2022. The Author(s).

Entities:  

Keywords:  05C05; 92D15

Mesh:

Year:  2022        PMID: 35552538      PMCID: PMC9098623          DOI: 10.1007/s00285-022-01748-w

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.164


  14 in total

1.  A note on efficient computation of haplotypes via perfect phylogeny.

Authors:  Vineet Bafna; Dan Gusfield; Sridhar Hannenhalli; Shibu Yooseph
Journal:  J Comput Biol       Date:  2004       Impact factor: 1.479

Review 2.  Modern computational approaches for analysing molecular genetic variation data.

Authors:  Paul Marjoram; Simon Tavaré
Journal:  Nat Rev Genet       Date:  2006-10       Impact factor: 53.242

3.  Enumeration of Ancestral Configurations for Matching Gene Trees and Species Trees.

Authors:  Filippo Disanto; Noah A Rosenberg
Journal:  J Comput Biol       Date:  2017-04-24       Impact factor: 1.479

4.  Finding the best resolution for the Kingman-Tajima coalescent: theory and applications.

Authors:  Raazesh Sainudiin; Tanja Stadler; Amandine Véber
Journal:  J Math Biol       Date:  2014-05-14       Impact factor: 2.259

5.  The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations.

Authors:  M Kimura
Journal:  Genetics       Date:  1969-04       Impact factor: 4.562

6.  SEQUENTIAL IMPORTANCE SAMPLING FOR MULTIRESOLUTION KINGMAN-TAJIMA COALESCENT COUNTING.

Authors:  Lorenzo Cappello; Julia A Palacios
Journal:  Ann Appl Stat       Date:  2020-06       Impact factor: 2.083

7.  Sampling theory for neutral alleles in a varying environment.

Authors:  R C Griffiths; S Tavaré
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1994-06-29       Impact factor: 6.237

8.  Bayesian phylogenetics with BEAUti and the BEAST 1.7.

Authors:  Alexei J Drummond; Marc A Suchard; Dong Xie; Andrew Rambaut
Journal:  Mol Biol Evol       Date:  2012-02-25       Impact factor: 16.240

9.  Bayesian Estimation of Population Size Changes by Sampling Tajima's Trees.

Authors:  Julia A Palacios; Amandine Véber; Lorenzo Cappello; Zhangyuan Wang; John Wakeley; Sohini Ramachandran
Journal:  Genetics       Date:  2019-09-11       Impact factor: 4.562

10.  Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies.

Authors:  Julia A Palacios; John Wakeley; Sohini Ramachandran
Journal:  Genetics       Date:  2015-07-28       Impact factor: 4.562

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