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Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses

A phylogeny is a tree-based model of common ancestry that is an indispensable tool for studying biological variation. Phylogenies play a special role in the study of rapidly evolving populations such as viruses, where the proliferation of lineages is constantly being shaped by the mode of virus tran...

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Autores principales: Poon, Art F. Y., Walker, Lorne W., Murray, Heather, McCloskey, Rosemary M., Harrigan, P. Richard, Liang, Richard H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3815201/
https://www.ncbi.nlm.nih.gov/pubmed/24223766
http://dx.doi.org/10.1371/journal.pone.0078122
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author Poon, Art F. Y.
Walker, Lorne W.
Murray, Heather
McCloskey, Rosemary M.
Harrigan, P. Richard
Liang, Richard H.
author_facet Poon, Art F. Y.
Walker, Lorne W.
Murray, Heather
McCloskey, Rosemary M.
Harrigan, P. Richard
Liang, Richard H.
author_sort Poon, Art F. Y.
collection PubMed
description A phylogeny is a tree-based model of common ancestry that is an indispensable tool for studying biological variation. Phylogenies play a special role in the study of rapidly evolving populations such as viruses, where the proliferation of lineages is constantly being shaped by the mode of virus transmission, by adaptation to immune systems, and by patterns of human migration and contact. These processes may leave an imprint on the shapes of virus phylogenies that can be extracted for comparative study; however, tree shapes are intrinsically difficult to quantify. Here we present a comprehensive study of phylogenies reconstructed from 38 different RNA viruses from 12 taxonomic families that are associated with human pathologies. To accomplish this, we have developed a new procedure for studying phylogenetic tree shapes based on the ‘kernel trick’, a technique that maps complex objects into a statistically convenient space. We show that our kernel method outperforms nine different tree balance statistics at correctly classifying phylogenies that were simulated under different evolutionary scenarios. Using the kernel method, we observe patterns in the distribution of RNA virus phylogenies in this space that reflect modes of transmission and pathogenesis. For example, viruses that can establish persistent chronic infections (such as HIV and hepatitis C virus) form a distinct cluster. Although the visibly ‘star-like’ shape characteristic of trees from these viruses has been well-documented, we show that established methods for quantifying tree shape fail to distinguish these trees from those of other viruses. The kernel approach presented here potentially represents an important new tool for characterizing the evolution and epidemiology of RNA viruses.
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spelling pubmed-38152012013-11-09 Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses Poon, Art F. Y. Walker, Lorne W. Murray, Heather McCloskey, Rosemary M. Harrigan, P. Richard Liang, Richard H. PLoS One Research Article A phylogeny is a tree-based model of common ancestry that is an indispensable tool for studying biological variation. Phylogenies play a special role in the study of rapidly evolving populations such as viruses, where the proliferation of lineages is constantly being shaped by the mode of virus transmission, by adaptation to immune systems, and by patterns of human migration and contact. These processes may leave an imprint on the shapes of virus phylogenies that can be extracted for comparative study; however, tree shapes are intrinsically difficult to quantify. Here we present a comprehensive study of phylogenies reconstructed from 38 different RNA viruses from 12 taxonomic families that are associated with human pathologies. To accomplish this, we have developed a new procedure for studying phylogenetic tree shapes based on the ‘kernel trick’, a technique that maps complex objects into a statistically convenient space. We show that our kernel method outperforms nine different tree balance statistics at correctly classifying phylogenies that were simulated under different evolutionary scenarios. Using the kernel method, we observe patterns in the distribution of RNA virus phylogenies in this space that reflect modes of transmission and pathogenesis. For example, viruses that can establish persistent chronic infections (such as HIV and hepatitis C virus) form a distinct cluster. Although the visibly ‘star-like’ shape characteristic of trees from these viruses has been well-documented, we show that established methods for quantifying tree shape fail to distinguish these trees from those of other viruses. The kernel approach presented here potentially represents an important new tool for characterizing the evolution and epidemiology of RNA viruses. Public Library of Science 2013-11-01 /pmc/articles/PMC3815201/ /pubmed/24223766 http://dx.doi.org/10.1371/journal.pone.0078122 Text en © 2013 Poon et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Poon, Art F. Y.
Walker, Lorne W.
Murray, Heather
McCloskey, Rosemary M.
Harrigan, P. Richard
Liang, Richard H.
Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title_full Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title_fullStr Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title_full_unstemmed Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title_short Mapping the Shapes of Phylogenetic Trees from Human and Zoonotic RNA Viruses
title_sort mapping the shapes of phylogenetic trees from human and zoonotic rna viruses
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3815201/
https://www.ncbi.nlm.nih.gov/pubmed/24223766
http://dx.doi.org/10.1371/journal.pone.0078122
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