BBCA: Improving the scalability of *BEAST using random binning

BMC Genomics

Research

Siavash Mirarab¹  Tandy Warnow²  Théo Zimmermann³ 

[1] Department of Computer Science, University of Texas, 78712, Austin, TX, USA;Department of Computer Science, University of Texas, 78712, Austin, TX, USA;Department of Bioengineering, The University of Illinois, 61801, Urbana, IL, USA;Department of Computer Science, University of Texas, 78712, Austin, TX, USA;Département d'Informatique, École Normale Supérieure, 75005, Paris, France;

关键词: multi-species coalescent;    phylogenomics;    incomplete lineage sorting;    binning;   

DOI  :  10.1186/1471-2164-15-S6-S11

来源: Springer

【 摘 要 】

BackgroundSpecies 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.ResultsWe 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.ConclusionsPhylogenomic 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.

【 授权许可】

Unknown   
© Zimmermann et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.