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Unblended disjoint tree merging using GTM improves species tree estimation

BACKGROUND: Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, c...

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Autores principales: Smirnov, Vladimir, Warnow, Tandy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7161100/
https://www.ncbi.nlm.nih.gov/pubmed/32299343
http://dx.doi.org/10.1186/s12864-020-6605-1
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author Smirnov, Vladimir
Warnow, Tandy
author_facet Smirnov, Vladimir
Warnow, Tandy
author_sort Smirnov, Vladimir
collection PubMed
description BACKGROUND: Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and then merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods. RESULTS: We present GTM (Guide Tree Merger), a polynomial time DTM method that adds edges to connect the subset trees, so as to provably minimize the topological distance to a computed guide tree. Thus, GTM performs unblended mergers, unlike the previous DTM methods. Yet, despite the potential limitation, our study shows that GTM has excellent accuracy, generally matching or improving on two previous DTMs, and is much faster than both. CONCLUSIONS: The proposed GTM approach to the DTM problem is a useful new tool for large-scale phylogenomic analysis, and shows the surprising potential for unblended DTM methods.
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spelling pubmed-71611002020-04-22 Unblended disjoint tree merging using GTM improves species tree estimation Smirnov, Vladimir Warnow, Tandy BMC Genomics Research BACKGROUND: Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and then merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods. RESULTS: We present GTM (Guide Tree Merger), a polynomial time DTM method that adds edges to connect the subset trees, so as to provably minimize the topological distance to a computed guide tree. Thus, GTM performs unblended mergers, unlike the previous DTM methods. Yet, despite the potential limitation, our study shows that GTM has excellent accuracy, generally matching or improving on two previous DTMs, and is much faster than both. CONCLUSIONS: The proposed GTM approach to the DTM problem is a useful new tool for large-scale phylogenomic analysis, and shows the surprising potential for unblended DTM methods. BioMed Central 2020-04-16 /pmc/articles/PMC7161100/ /pubmed/32299343 http://dx.doi.org/10.1186/s12864-020-6605-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Smirnov, Vladimir
Warnow, Tandy
Unblended disjoint tree merging using GTM improves species tree estimation
title Unblended disjoint tree merging using GTM improves species tree estimation
title_full Unblended disjoint tree merging using GTM improves species tree estimation
title_fullStr Unblended disjoint tree merging using GTM improves species tree estimation
title_full_unstemmed Unblended disjoint tree merging using GTM improves species tree estimation
title_short Unblended disjoint tree merging using GTM improves species tree estimation
title_sort unblended disjoint tree merging using gtm improves species tree estimation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7161100/
https://www.ncbi.nlm.nih.gov/pubmed/32299343
http://dx.doi.org/10.1186/s12864-020-6605-1
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