BBCA: Improving the scalability of *BEAST using random binning
| Autor: | Siavash Mirarab, Théo Zimmermann, Tandy Warnow |
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| Rok vydání: | 2015 |
| Předmět: |
0106 biological sciences
Genetic Speciation Bayesian probability Datasets as Topic Biology computer.software_genre 010603 evolutionary biology 01 natural sciences Coalescent theory 03 medical and health sciences Phylogenomics Genetics Phylogeny 030304 developmental biology 0303 health sciences Sequence Models Genetic Population size Small number Research incomplete lineage sorting Bayes Theorem phylogenomics Genomics Tree (data structure) binning Scalability multi-species coalescent Data mining computer Biotechnology |
| Zdroj: | BMC Genomics |
| ISSN: | 1471-2164 |
| Popis: | Background Species tree estimation can be challenging in the presence of gene tree conflict due to incomplete lineage sorting (ILS), which can occur when the time between speciation events is short relative to the population size. Of the many methods that have been developed to estimate species trees in the presence of ILS, *BEAST, a Bayesian method that co-estimates the species tree and gene trees given sequence alignments on multiple loci, has generally been shown to have the best accuracy. However, *BEAST is extremely computationally intensive so that it cannot be used with large numbers of loci; hence, *BEAST is not suitable for genome-scale analyses. Results We present BBCA (boosted binned coalescent-based analysis), a method that can be used with *BEAST (and other such co-estimation methods) to improve scalability. BBCA partitions the loci randomly into subsets, uses *BEAST on each subset to co-estimate the gene trees and species tree for the subset, and then combines the newly estimated gene trees together using MP-EST, a popular coalescent-based summary method. We compare time-restricted versions of BBCA and *BEAST on simulated datasets, and show that BBCA is at least as accurate as *BEAST, and achieves better convergence rates for large numbers of loci. Conclusions Phylogenomic analysis using *BEAST is currently limited to datasets with a small number of loci, and analyses with even just 100 loci can be computationally challenging. BBCA uses a very simple divide-and-conquer approach that makes it possible to use *BEAST on datasets containing hundreds of loci. This study shows that BBCA provides excellent accuracy and is highly scalable. |
| Databáze: | OpenAIRE |
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