TreeMerge: a new method for improving the scalability of species tree estimation methods.
Bioinformatics
; 35(14): i417-i426, 2019 07 15.
Article
em En
| MEDLINE
| ID: mdl-31510668
ABSTRACT
MOTIVATION At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations:
it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species.RESULTS:
Here we present a new method called 'TreeMerge' that improves on NJMerge in two ways it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework-only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. AVAILABILITY AND IMPLEMENTATION TreeMerge is publicly available on Github (http//github.com/ekmolloy/treemerge). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Filogenia
/
Algoritmos
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos