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PRAP: Pan Resistome analysis pipeline
BACKGROUND: Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs dis...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964052/ https://www.ncbi.nlm.nih.gov/pubmed/31941435 http://dx.doi.org/10.1186/s12859-019-3335-y |
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author | He, Yichen Zhou, Xiujuan Chen, Ziyan Deng, Xiangyu Gehring, Andrew Ou, Hongyu Zhang, Lida Shi, Xianming |
author_facet | He, Yichen Zhou, Xiujuan Chen, Ziyan Deng, Xiangyu Gehring, Andrew Ou, Hongyu Zhang, Lida Shi, Xianming |
author_sort | He, Yichen |
collection | PubMed |
description | BACKGROUND: Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions. RESULTS: We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China. CONCLUSIONS: PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits. |
format | Online Article Text |
id | pubmed-6964052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69640522020-01-22 PRAP: Pan Resistome analysis pipeline He, Yichen Zhou, Xiujuan Chen, Ziyan Deng, Xiangyu Gehring, Andrew Ou, Hongyu Zhang, Lida Shi, Xianming BMC Bioinformatics Software BACKGROUND: Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions. RESULTS: We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China. CONCLUSIONS: PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits. BioMed Central 2020-01-15 /pmc/articles/PMC6964052/ /pubmed/31941435 http://dx.doi.org/10.1186/s12859-019-3335-y Text en © The Author(s). 2020 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 He, Yichen Zhou, Xiujuan Chen, Ziyan Deng, Xiangyu Gehring, Andrew Ou, Hongyu Zhang, Lida Shi, Xianming PRAP: Pan Resistome analysis pipeline |
title | PRAP: Pan Resistome analysis pipeline |
title_full | PRAP: Pan Resistome analysis pipeline |
title_fullStr | PRAP: Pan Resistome analysis pipeline |
title_full_unstemmed | PRAP: Pan Resistome analysis pipeline |
title_short | PRAP: Pan Resistome analysis pipeline |
title_sort | prap: pan resistome analysis pipeline |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964052/ https://www.ncbi.nlm.nih.gov/pubmed/31941435 http://dx.doi.org/10.1186/s12859-019-3335-y |
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