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Phylogenetic search through partial tree mixing

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic al...

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Detalles Bibliográficos
Autores principales: Sundberg, Kenneth, Clement, Mark, Snell, Quinn, Ventura, Dan, Whiting, Michael, Crandall, Keith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3426809/
https://www.ncbi.nlm.nih.gov/pubmed/23320449
http://dx.doi.org/10.1186/1471-2105-13-S13-S8
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author Sundberg, Kenneth
Clement, Mark
Snell, Quinn
Ventura, Dan
Whiting, Michael
Crandall, Keith
author_facet Sundberg, Kenneth
Clement, Mark
Snell, Quinn
Ventura, Dan
Whiting, Michael
Crandall, Keith
author_sort Sundberg, Kenneth
collection PubMed
description BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques. RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda CONCLUSIONS: The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution.
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spelling pubmed-34268092012-08-24 Phylogenetic search through partial tree mixing Sundberg, Kenneth Clement, Mark Snell, Quinn Ventura, Dan Whiting, Michael Crandall, Keith BMC Bioinformatics Research BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques. RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda CONCLUSIONS: The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution. BioMed Central 2012-08-24 /pmc/articles/PMC3426809/ /pubmed/23320449 http://dx.doi.org/10.1186/1471-2105-13-S13-S8 Text en Copyright ©2012 Sundberg et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Sundberg, Kenneth
Clement, Mark
Snell, Quinn
Ventura, Dan
Whiting, Michael
Crandall, Keith
Phylogenetic search through partial tree mixing
title Phylogenetic search through partial tree mixing
title_full Phylogenetic search through partial tree mixing
title_fullStr Phylogenetic search through partial tree mixing
title_full_unstemmed Phylogenetic search through partial tree mixing
title_short Phylogenetic search through partial tree mixing
title_sort phylogenetic search through partial tree mixing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3426809/
https://www.ncbi.nlm.nih.gov/pubmed/23320449
http://dx.doi.org/10.1186/1471-2105-13-S13-S8
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