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ResFinder 4.0 for predictions of phenotypes from genotypes
OBJECTIVES: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determin...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662176/ https://www.ncbi.nlm.nih.gov/pubmed/32780112 http://dx.doi.org/10.1093/jac/dkaa345 |
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author | Bortolaia, Valeria Kaas, Rolf S Ruppe, Etienne Roberts, Marilyn C Schwarz, Stefan Cattoir, Vincent Philippon, Alain Allesoe, Rosa L Rebelo, Ana Rita Florensa, Alfred Ferrer Fagelhauer, Linda Chakraborty, Trinad Neumann, Bernd Werner, Guido Bender, Jennifer K Stingl, Kerstin Nguyen, Minh Coppens, Jasmine Xavier, Basil Britto Malhotra-Kumar, Surbhi Westh, Henrik Pinholt, Mette Anjum, Muna F Duggett, Nicholas A Kempf, Isabelle Nykäsenoja, Suvi Olkkola, Satu Wieczorek, Kinga Amaro, Ana Clemente, Lurdes Mossong, Joël Losch, Serge Ragimbeau, Catherine Lund, Ole Aarestrup, Frank M |
author_facet | Bortolaia, Valeria Kaas, Rolf S Ruppe, Etienne Roberts, Marilyn C Schwarz, Stefan Cattoir, Vincent Philippon, Alain Allesoe, Rosa L Rebelo, Ana Rita Florensa, Alfred Ferrer Fagelhauer, Linda Chakraborty, Trinad Neumann, Bernd Werner, Guido Bender, Jennifer K Stingl, Kerstin Nguyen, Minh Coppens, Jasmine Xavier, Basil Britto Malhotra-Kumar, Surbhi Westh, Henrik Pinholt, Mette Anjum, Muna F Duggett, Nicholas A Kempf, Isabelle Nykäsenoja, Suvi Olkkola, Satu Wieczorek, Kinga Amaro, Ana Clemente, Lurdes Mossong, Joël Losch, Serge Ragimbeau, Catherine Lund, Ole Aarestrup, Frank M |
author_sort | Bortolaia, Valeria |
collection | PubMed |
description | OBJECTIVES: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. METHODS: The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. RESULTS: Genotype–phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype–phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. CONCLUSIONS: WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered. |
format | Online Article Text |
id | pubmed-7662176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76621762020-11-18 ResFinder 4.0 for predictions of phenotypes from genotypes Bortolaia, Valeria Kaas, Rolf S Ruppe, Etienne Roberts, Marilyn C Schwarz, Stefan Cattoir, Vincent Philippon, Alain Allesoe, Rosa L Rebelo, Ana Rita Florensa, Alfred Ferrer Fagelhauer, Linda Chakraborty, Trinad Neumann, Bernd Werner, Guido Bender, Jennifer K Stingl, Kerstin Nguyen, Minh Coppens, Jasmine Xavier, Basil Britto Malhotra-Kumar, Surbhi Westh, Henrik Pinholt, Mette Anjum, Muna F Duggett, Nicholas A Kempf, Isabelle Nykäsenoja, Suvi Olkkola, Satu Wieczorek, Kinga Amaro, Ana Clemente, Lurdes Mossong, Joël Losch, Serge Ragimbeau, Catherine Lund, Ole Aarestrup, Frank M J Antimicrob Chemother Original Research OBJECTIVES: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. METHODS: The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. RESULTS: Genotype–phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype–phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. CONCLUSIONS: WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered. Oxford University Press 2020-08-11 /pmc/articles/PMC7662176/ /pubmed/32780112 http://dx.doi.org/10.1093/jac/dkaa345 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. 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 | Original Research Bortolaia, Valeria Kaas, Rolf S Ruppe, Etienne Roberts, Marilyn C Schwarz, Stefan Cattoir, Vincent Philippon, Alain Allesoe, Rosa L Rebelo, Ana Rita Florensa, Alfred Ferrer Fagelhauer, Linda Chakraborty, Trinad Neumann, Bernd Werner, Guido Bender, Jennifer K Stingl, Kerstin Nguyen, Minh Coppens, Jasmine Xavier, Basil Britto Malhotra-Kumar, Surbhi Westh, Henrik Pinholt, Mette Anjum, Muna F Duggett, Nicholas A Kempf, Isabelle Nykäsenoja, Suvi Olkkola, Satu Wieczorek, Kinga Amaro, Ana Clemente, Lurdes Mossong, Joël Losch, Serge Ragimbeau, Catherine Lund, Ole Aarestrup, Frank M ResFinder 4.0 for predictions of phenotypes from genotypes |
title | ResFinder 4.0 for predictions of phenotypes from genotypes |
title_full | ResFinder 4.0 for predictions of phenotypes from genotypes |
title_fullStr | ResFinder 4.0 for predictions of phenotypes from genotypes |
title_full_unstemmed | ResFinder 4.0 for predictions of phenotypes from genotypes |
title_short | ResFinder 4.0 for predictions of phenotypes from genotypes |
title_sort | resfinder 4.0 for predictions of phenotypes from genotypes |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662176/ https://www.ncbi.nlm.nih.gov/pubmed/32780112 http://dx.doi.org/10.1093/jac/dkaa345 |
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