Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: He, Yichen, Zhou, Xiujuan, Chen, Ziyan, Deng, Xiangyu, Gehring, Andrew, Ou, Hongyu, Zhang, Lida, Shi, Xianming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
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
_version_ 1783488422780862464
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
work_keys_str_mv AT heyichen prappanresistomeanalysispipeline
AT zhouxiujuan prappanresistomeanalysispipeline
AT chenziyan prappanresistomeanalysispipeline
AT dengxiangyu prappanresistomeanalysispipeline
AT gehringandrew prappanresistomeanalysispipeline
AT ouhongyu prappanresistomeanalysispipeline
AT zhanglida prappanresistomeanalysispipeline
AT shixianming prappanresistomeanalysispipeline