Your browser doesn't support javascript.
loading
ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy.
Zhang, Chao; Scornavacca, Celine; Molloy, Erin K; Mirarab, Siavash.
Afiliación
  • Zhang C; Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA.
  • Scornavacca C; ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France.
  • Molloy EK; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL.
  • Mirarab S; Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA.
Mol Biol Evol ; 37(11): 3292-3307, 2020 11 01.
Article en En | MEDLINE | ID: mdl-32886770
ABSTRACT
Phylogenetic inference from genome-wide data (phylogenomics) has revolutionized the study of evolution because it enables accounting for discordance among evolutionary histories across the genome. To this end, summary methods have been developed to allow accurate and scalable inference of species trees from gene trees. However, most of these methods, including the widely used ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. As a result, most phylogenomic studies have focused on single-copy genes and have discarded large parts of the data. Here, we first propose a measure of quartet similarity between single-copy and multicopy trees that accounts for orthology and paralogy. We then introduce a method called ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs) to find the species tree that optimizes our quartet similarity measure using dynamic programing. By studying its performance on an extensive collection of simulated data sets and on real data sets, we show that ASTRAL-Pro is more accurate than alternative methods.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Filogenia / Técnicas Genéticas Tipo de estudio: Evaluation_studies / Prognostic_studies Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: Canadá

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Filogenia / Técnicas Genéticas Tipo de estudio: Evaluation_studies / Prognostic_studies Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: Canadá