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dc.creatorZimmermann, Theoen_US
dc.creatorMirarab, Siavashen_US
dc.creatorWarnow, Tandyen_US
dc.date.accessioned2016-10-28T19:49:33Z
dc.date.available2016-10-28T19:49:33Z
dc.date.issued2014-10en_US
dc.identifierdoi:10.15781/T2445HF8G
dc.identifier.citationZimmermann, Théo, Siavash Mirarab, and Tandy Warnow. "BBCA: Improving the scalability of* BEAST using random binning." BMC genomics, Vol. 15, No. Suppl 6 (Oct., 2014): S11.en_US
dc.identifier.issn1471-2164en_US
dc.identifier.urihttp://hdl.handle.net/2152/43152
dc.description.abstractSpecies 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.en_US
dc.description.sponsorshipGrant Agency of the Czech Republic P501-10-0208en_US
dc.description.sponsorshipAcademy of Sciences of the Czech Republic AVOZ50040507, AVOZ50040702, MSMT LC0604en_US
dc.description.sponsorshipMinistry of Innovation and Science of Spain, MICINN CGL2007-64839-C02/BOSen_US
dc.description.sponsorshipCSIC (Superior Council of Scientific Investigationsen_US
dc.description.sponsorshipJosé Castillejo Grant from the MICINN of the Spanish Governmenten_US
dc.language.isoEnglishen_US
dc.relation.ispartofen_US
dc.rightsAdministrative deposit of works to Texas ScholarWorks: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access or the publisher allows a PDF version of the article to be freely posted online. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en_US
dc.subjectmultispecies coalescent modelen_US
dc.subjectspecies tree estimationen_US
dc.subjectgene treesen_US
dc.subjectmaximum-likelihooden_US
dc.subjectaccuracyen_US
dc.subjectbiotechnology & applied microbiologyen_US
dc.subjectgenetics & heredityen_US
dc.titleBBCA: Improving the Scalability of *BEAST Using Random Binningen_US
dc.typeArticle; Proceedings Paperen_US
dc.description.departmentComputer Sciencesen_US
dc.rights.restrictionOpenen_US
dc.identifier.doi10.1186/1471-2164-15-s6-s11en_US
dc.contributor.utaustinauthorZimmermann, Theoen_US
dc.contributor.utaustinauthorMirarab, Siavashen_US
dc.contributor.utaustinauthorWarnow, Tandyen_US
dc.relation.ispartofserialBMC Genomicsen_US


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