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MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs
The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199892/ https://www.ncbi.nlm.nih.gov/pubmed/30247672 http://dx.doi.org/10.1093/gbe/evy209 |
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author | Corel, Eduardo Pathmanathan, Jananan S Watson, Andrew K Karkar, Slim Lopez, Philippe Bapteste, Eric |
author_facet | Corel, Eduardo Pathmanathan, Jananan S Watson, Andrew K Karkar, Slim Lopez, Philippe Bapteste, Eric |
author_sort | Corel, Eduardo |
collection | PubMed |
description | The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family–genome graphs) for the analysis of pathogenicity traits in prokaryotes. |
format | Online Article Text |
id | pubmed-6199892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61998922018-10-29 MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs Corel, Eduardo Pathmanathan, Jananan S Watson, Andrew K Karkar, Slim Lopez, Philippe Bapteste, Eric Genome Biol Evol Genome Resources The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family–genome graphs) for the analysis of pathogenicity traits in prokaryotes. Oxford University Press 2018-09-22 /pmc/articles/PMC6199892/ /pubmed/30247672 http://dx.doi.org/10.1093/gbe/evy209 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Genome Resources Corel, Eduardo Pathmanathan, Jananan S Watson, Andrew K Karkar, Slim Lopez, Philippe Bapteste, Eric MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title |
MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title_full |
MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title_fullStr |
MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title_full_unstemmed |
MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title_short |
MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs |
title_sort | multitwin: a software suite to analyze evolution at multiple levels of organization using multipartite graphs |
topic | Genome Resources |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199892/ https://www.ncbi.nlm.nih.gov/pubmed/30247672 http://dx.doi.org/10.1093/gbe/evy209 |
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