SpeciesRax: A Tool for Maximum Likelihood Species Tree Inference from Gene Family Trees under Duplication, Transfer, and Loss.
gene duplication
gene family tree
gene loss
horizontal gene transfer
maximum likelihood
species tree inference
Journal
Molecular biology and evolution
ISSN: 1537-1719
Titre abrégé: Mol Biol Evol
Pays: United States
ID NLM: 8501455
Informations de publication
Date de publication:
03 02 2022
03 02 2022
Historique:
pubmed:
13
1
2022
medline:
1
4
2022
entrez:
12
1
2022
Statut:
ppublish
Résumé
Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modeling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated data sets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large data sets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising 188 species from 31,612 gene families in 1 h using 40 cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.
Identifiants
pubmed: 35021210
pii: 6503503
doi: 10.1093/molbev/msab365
pmc: PMC8826479
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
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