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Exploring Evolutionary Relationships Across the Genome Using Topology Weighting

We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualizing how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represente...

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Detalles Bibliográficos
Autores principales: Martin, Simon H., Van Belleghem, Steven M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419486/
https://www.ncbi.nlm.nih.gov/pubmed/28341652
http://dx.doi.org/10.1534/genetics.116.194720
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author Martin, Simon H.
Van Belleghem, Steven M.
author_facet Martin, Simon H.
Van Belleghem, Steven M.
author_sort Martin, Simon H.
collection PubMed
description We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualizing how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represented by multiple sequences, the number of possible topologies becomes very large. Topology weighting reduces this complexity by quantifying the contribution of each taxon topology to the full tree. We describe our method for topology weighting by iterative sampling of subtrees (Twisst), and test it on both simulated and real genomic data. Overall, we show that this is an informative and versatile approach, suitable for exploring relationships in almost any genomic dataset. Scripts to implement the method described are available at http://github.com/simonhmartin/twisst.
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spelling pubmed-54194862018-05-01 Exploring Evolutionary Relationships Across the Genome Using Topology Weighting Martin, Simon H. Van Belleghem, Steven M. Genetics Investigations We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualizing how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represented by multiple sequences, the number of possible topologies becomes very large. Topology weighting reduces this complexity by quantifying the contribution of each taxon topology to the full tree. We describe our method for topology weighting by iterative sampling of subtrees (Twisst), and test it on both simulated and real genomic data. Overall, we show that this is an informative and versatile approach, suitable for exploring relationships in almost any genomic dataset. Scripts to implement the method described are available at http://github.com/simonhmartin/twisst. Genetics Society of America 2017-05 2017-03-23 /pmc/articles/PMC5419486/ /pubmed/28341652 http://dx.doi.org/10.1534/genetics.116.194720 Text en Copyright © 2017 Martin et al. Available freely online through the author-supported open access option. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International 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.
spellingShingle Investigations
Martin, Simon H.
Van Belleghem, Steven M.
Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title_full Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title_fullStr Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title_full_unstemmed Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title_short Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
title_sort exploring evolutionary relationships across the genome using topology weighting
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419486/
https://www.ncbi.nlm.nih.gov/pubmed/28341652
http://dx.doi.org/10.1534/genetics.116.194720
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