Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3882
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Maximum-likelihood tree estimation using codon substitution models with multiple partitions
Authors: Zoller, Stefan
Boskova, Veronika
Anisimova, Maria
DOI: 10.21256/zhaw-3882
10.1093/molbev/msv097
Published in: Molecular Biology and Evolution
Volume(Issue): 32
Issue: 8
Page(s): 2208
Pages to: 2216
Issue Date: 2015
Publisher / Ed. Institution: Oxford University Press
ISSN: 0737-4038
1537-1719
Language: English
Subjects: Markov model; Amino acid substitution model; Codon substitution model; Maximum-likelihood tree; Bacterial protein; Ralstonia solanacearum; Codon; Genetic model
Subject (DDC): 572: Biochemistry
Abstract: Many protein sequences have distinct domains that evolve with different rates, different selective pressures, or may differ in codon bias. Instead of modeling these differences by more and more complex models of molecular evolution, we present a multipartition approach that allows maximum-likelihood phylogeny inference using different codon models at predefined partitions in the data. Partition models can, but do not have to, share free parameters in the estimation process. We test this approach with simulated data as well as in a phylogenetic study of the origin of the leucin-rich repeat regions in the type III effector proteins of the pythopathogenic bacteria Ralstonia solanacearum. Our study does not only show that a simple two-partition model resolves the phylogeny better than a one-partition model but also gives more evidence supporting the hypothesis of lateral gene transfer events between the bacterial pathogens and its eukaryotic hosts.
Further description: Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)
URI: https://digitalcollection.zhaw.ch/handle/11475/8260
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Restricted until: 2019-01-01
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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