A machine-learning-based alternative to phylogenetic bootstrap.
Bioinformatics
; 40(Supplement_1): i208-i217, 2024 Jun 28.
Article
em En
| MEDLINE
| ID: mdl-38940166
ABSTRACT
MOTIVATION Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance. RESULTS:
Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets. AVAILABILITY AND IMPLEMENTATION The data supporting this work are available in the Figshare repository at https//doi.org/10.6084/m9.figshare.25050554.v1, and the underlying code is accessible via GitHub at https//github.com/noaeker/bootstrap_repo.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Filogenia
/
Algoritmos
/
Software
/
Aprendizado de Máquina
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Israel