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Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity
BACKGROUND: Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. RESULTS: Here we...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930393/ https://www.ncbi.nlm.nih.gov/pubmed/29716518 http://dx.doi.org/10.1186/s12859-018-2164-8 |
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author | Menardo, Fabrizio Loiseau, Chloé Brites, Daniela Coscolla, Mireia Gygli, Sebastian M. Rutaihwa, Liliana K. Trauner, Andrej Beisel, Christian Borrell, Sonia Gagneux, Sebastien |
author_facet | Menardo, Fabrizio Loiseau, Chloé Brites, Daniela Coscolla, Mireia Gygli, Sebastian M. Rutaihwa, Liliana K. Trauner, Andrej Beisel, Christian Borrell, Sonia Gagneux, Sebastien |
author_sort | Menardo, Fabrizio |
collection | PubMed |
description | BACKGROUND: Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. RESULTS: Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. CONCLUSIONS: Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2164-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5930393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59303932018-05-09 Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity Menardo, Fabrizio Loiseau, Chloé Brites, Daniela Coscolla, Mireia Gygli, Sebastian M. Rutaihwa, Liliana K. Trauner, Andrej Beisel, Christian Borrell, Sonia Gagneux, Sebastien BMC Bioinformatics Software BACKGROUND: Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. RESULTS: Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. CONCLUSIONS: Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2164-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-02 /pmc/articles/PMC5930393/ /pubmed/29716518 http://dx.doi.org/10.1186/s12859-018-2164-8 Text en © The Author(s). 2018 Open AccessThis article is 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 you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Software Menardo, Fabrizio Loiseau, Chloé Brites, Daniela Coscolla, Mireia Gygli, Sebastian M. Rutaihwa, Liliana K. Trauner, Andrej Beisel, Christian Borrell, Sonia Gagneux, Sebastien Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title | Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title_full | Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title_fullStr | Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title_full_unstemmed | Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title_short | Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
title_sort | treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930393/ https://www.ncbi.nlm.nih.gov/pubmed/29716518 http://dx.doi.org/10.1186/s12859-018-2164-8 |
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