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An object-oriented framework for evolutionary pangenome analysis
Pangenome analysis is fundamental to explore molecular evolution occurring in bacterial populations. Here, we introduce Pagoo, an R framework that enables straightforward handling of pangenome data. The encapsulated nature of Pagoo allows the storage of complex molecular and phenotypic information u...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017228/ https://www.ncbi.nlm.nih.gov/pubmed/35474671 http://dx.doi.org/10.1016/j.crmeth.2021.100085 |
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author | Ferrés, Ignacio Iraola, Gregorio |
author_facet | Ferrés, Ignacio Iraola, Gregorio |
author_sort | Ferrés, Ignacio |
collection | PubMed |
description | Pangenome analysis is fundamental to explore molecular evolution occurring in bacterial populations. Here, we introduce Pagoo, an R framework that enables straightforward handling of pangenome data. The encapsulated nature of Pagoo allows the storage of complex molecular and phenotypic information using an object-oriented approach. This facilitates to go back and forward to the data using a single programming environment and saving any stage of analysis (including the raw data) in a single file, making it sharable and reproducible. Pagoo provides tools to query, subset, compare, visualize, and perform statistical analyses, in concert with other microbial genomics packages available in the R ecosystem. As working examples, we used 1,000 Escherichia coli genomes to show that Pagoo is scalable, and a global dataset of Campylobacter fetus genomes to identify evolutionary patterns and genomic markers of host-adaptation in this pathogen. |
format | Online Article Text |
id | pubmed-9017228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-90172282022-04-25 An object-oriented framework for evolutionary pangenome analysis Ferrés, Ignacio Iraola, Gregorio Cell Rep Methods Report Pangenome analysis is fundamental to explore molecular evolution occurring in bacterial populations. Here, we introduce Pagoo, an R framework that enables straightforward handling of pangenome data. The encapsulated nature of Pagoo allows the storage of complex molecular and phenotypic information using an object-oriented approach. This facilitates to go back and forward to the data using a single programming environment and saving any stage of analysis (including the raw data) in a single file, making it sharable and reproducible. Pagoo provides tools to query, subset, compare, visualize, and perform statistical analyses, in concert with other microbial genomics packages available in the R ecosystem. As working examples, we used 1,000 Escherichia coli genomes to show that Pagoo is scalable, and a global dataset of Campylobacter fetus genomes to identify evolutionary patterns and genomic markers of host-adaptation in this pathogen. Elsevier 2021-09-27 /pmc/articles/PMC9017228/ /pubmed/35474671 http://dx.doi.org/10.1016/j.crmeth.2021.100085 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Report Ferrés, Ignacio Iraola, Gregorio An object-oriented framework for evolutionary pangenome analysis |
title | An object-oriented framework for evolutionary pangenome analysis |
title_full | An object-oriented framework for evolutionary pangenome analysis |
title_fullStr | An object-oriented framework for evolutionary pangenome analysis |
title_full_unstemmed | An object-oriented framework for evolutionary pangenome analysis |
title_short | An object-oriented framework for evolutionary pangenome analysis |
title_sort | object-oriented framework for evolutionary pangenome analysis |
topic | Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017228/ https://www.ncbi.nlm.nih.gov/pubmed/35474671 http://dx.doi.org/10.1016/j.crmeth.2021.100085 |
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