GTRpmix: A linked general-time reversible model for profile mixture models.

General Time Reversible Molecular Evolution Phylogenetics Profile Mixture Model

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:
19 Aug 2024
Historique:
received: 05 04 2024
revised: 25 06 2024
accepted: 15 08 2024
medline: 19 8 2024
pubmed: 19 8 2024
entrez: 19 8 2024
Statut: aheadofprint

Résumé

Profile mixture models capture distinct biochemical constraints on the amino acid substitution process at different sites in proteins. These models feature a mixture of time-reversible models with a common matrix of exchangeabilities and distinct sets of equilibrium amino acid frequencies known as profiles. Combining the exchangeability matrix with each profile generates the matrix of instantaneous rates of amino acid exchange for that profile. Currently, empirically estimated exchangeability matrices (e.g., the LG matrix) are widely used for phylogenetic inference under profile mixture models. However, these were estimated using a single profile and are unlikely optimal for profile mixture models. Here, we describe the GTRpmix model that allows maximum likelihood estimation of a common exchangeability matrix under any profile mixture model. We show that exchangeability matrices estimated under profile mixture models differ from the LG matrix, dramatically improving model fit and topological estimation accuracy for empirical test cases. Because the GTRpmix model is computationally expensive, we provide two exchangeability matrices estimated from large concatenated phylogenomic-supermatrices to be used for phylogenetic analyses. One, called Eukaryotic Linked Mixture (ELM), is designed for phylogenetic analysis of proteins encoded by nuclear genomes of eukaryotes, and the other, Eukaryotic and Archaeal Linked mixture (EAL), for reconstructing relationships between eukaryotes and Archaea. These matrices, combined with profile mixture models, fit data better and have improved topology estimation relative to the LG matrix combined with the same mixture models. Starting with version 2.3.1, IQ-TREE2 allows users to estimate linked exchangeabilities (i.e. amino acid exchange rates) under profile mixture models.

Identifiants

pubmed: 39158305
pii: 7735827
doi: 10.1093/molbev/msae174
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

Auteurs

Hector Banos (H)

Department of Mathematics, California State University San Bernardino, San Bernardino, CA, USA.
Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.

Thomas K F Wong (TKF)

School of Computing, College of Engineering and Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia.
Ecology and Evolution, Research School of Biology, College of Science, Australian National University, Canberra, ACT 2600, Australia.

Justin Daneau (J)

Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.

Edward Susko (E)

Department of Mathematics and Statistics, Faculty of Science, Dalhousie University, Halifax, NS, Canada.

Bui Quang Minh (BQ)

School of Computing, College of Engineering and Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia.

Robert Lanfear (R)

Ecology and Evolution, Research School of Biology, College of Science, Australian National University, Canberra, ACT 2600, Australia.

Matthew W Brown (MW)

Department of Biological Sciences, Mississippi State University, Mississippi State, MS, USA.

Laura Eme (L)

Université Paris-Saclay, Laboratoire d'Ecologie, systématique et Evolution, Gif-sur-Yvette, France.

Andrew J Roger (AJ)

Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.

Classifications MeSH