Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset
Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detectio...
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sg-ntu-dr.10356-1476032023-12-29T06:49:35Z Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset Höper, Dirk Grützke, Josephine Brinkmann, Annika Mossong, Joël Matamoros, Sébastien Ellis, Richard J. Deneke, Carlus Tausch, Simon H. Cuesta, Isabel Monzón, Sara Juliá, Miguel Petersen, Thomas Nordahl Hendriksen, Rene S. Pamp, Sünje J. Leijon, Mikael Hakhverdyan, Mikhayil Walsh, Aaron M. Cotter, Paul D. Chandrasekaran, Lakshmi Tay, Moon Yue Feng Schlundt, Joergen Sala, Claudia De Cesare, Alessandra Nitsche, Andreas Beer, Martin Wylezich, Claudia School of Chemical and Biomedical Engineering NTU Food Technology Centre Engineering::Bioengineering Background Contamination Diagnostic Assessment Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants' reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary. Published version 2021-04-13T08:59:56Z 2021-04-13T08:59:56Z 2020 Journal Article Höper, D., Grützke, J., Brinkmann, A., Mossong, J., Matamoros, S., Ellis, R. J., Deneke, C., Tausch, S. H., Cuesta, I., Monzón, S., Juliá, M., Petersen, T. N., Hendriksen, R. S., Pamp, S. J., Leijon, M., Hakhverdyan, M., Walsh, A. M., Cotter, P. D., Chandrasekaran, L., ...Wylezich, C. (2020). Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset. Frontiers in Microbiology, 11. https://dx.doi.org/10.3389/fmicb.2020.575377 1664-302X https://hdl.handle.net/10356/147603 10.3389/fmicb.2020.575377 33250869 2-s2.0-85096197743 11 en Frontiers in Microbiology © 2020 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. application/pdf |
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Engineering::Bioengineering Background Contamination Diagnostic Assessment Höper, Dirk Grützke, Josephine Brinkmann, Annika Mossong, Joël Matamoros, Sébastien Ellis, Richard J. Deneke, Carlus Tausch, Simon H. Cuesta, Isabel Monzón, Sara Juliá, Miguel Petersen, Thomas Nordahl Hendriksen, Rene S. Pamp, Sünje J. Leijon, Mikael Hakhverdyan, Mikhayil Walsh, Aaron M. Cotter, Paul D. Chandrasekaran, Lakshmi Tay, Moon Yue Feng Schlundt, Joergen Sala, Claudia De Cesare, Alessandra Nitsche, Andreas Beer, Martin Wylezich, Claudia Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
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Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants' reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Höper, Dirk Grützke, Josephine Brinkmann, Annika Mossong, Joël Matamoros, Sébastien Ellis, Richard J. Deneke, Carlus Tausch, Simon H. Cuesta, Isabel Monzón, Sara Juliá, Miguel Petersen, Thomas Nordahl Hendriksen, Rene S. Pamp, Sünje J. Leijon, Mikael Hakhverdyan, Mikhayil Walsh, Aaron M. Cotter, Paul D. Chandrasekaran, Lakshmi Tay, Moon Yue Feng Schlundt, Joergen Sala, Claudia De Cesare, Alessandra Nitsche, Andreas Beer, Martin Wylezich, Claudia |
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Article |
author |
Höper, Dirk Grützke, Josephine Brinkmann, Annika Mossong, Joël Matamoros, Sébastien Ellis, Richard J. Deneke, Carlus Tausch, Simon H. Cuesta, Isabel Monzón, Sara Juliá, Miguel Petersen, Thomas Nordahl Hendriksen, Rene S. Pamp, Sünje J. Leijon, Mikael Hakhverdyan, Mikhayil Walsh, Aaron M. Cotter, Paul D. Chandrasekaran, Lakshmi Tay, Moon Yue Feng Schlundt, Joergen Sala, Claudia De Cesare, Alessandra Nitsche, Andreas Beer, Martin Wylezich, Claudia |
author_sort |
Höper, Dirk |
title |
Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
title_short |
Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
title_full |
Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
title_fullStr |
Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
title_full_unstemmed |
Proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
title_sort |
proficiency testing of metagenomics-based detection of food-borne pathogens using a complex artificial sequencing dataset |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/147603 |
_version_ |
1787136612254089216 |