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Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing

The use of whole genome sequencing (WGS) data generated by the long-read sequencing platform Oxford Nanopore Technologies (ONT) has been shown to provide reliable results for Salmonella serotype prediction in a previous study. To further meet the needs of industry for accurate, rapid, and cost-effic...

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Autores principales: Wu, Xingwen, Luo, Hao, Xu, Feng, Ge, Chongtao, Li, Shaoting, Deng, Xiangyu, Wiedmann, Martin, Baker, Robert C., Stevenson, Abigail, Zhang, Guangtao, Tang, Silin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987803/
https://www.ncbi.nlm.nih.gov/pubmed/33776971
http://dx.doi.org/10.3389/fmicb.2021.637771
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author Wu, Xingwen
Luo, Hao
Xu, Feng
Ge, Chongtao
Li, Shaoting
Deng, Xiangyu
Wiedmann, Martin
Baker, Robert C.
Stevenson, Abigail
Zhang, Guangtao
Tang, Silin
author_facet Wu, Xingwen
Luo, Hao
Xu, Feng
Ge, Chongtao
Li, Shaoting
Deng, Xiangyu
Wiedmann, Martin
Baker, Robert C.
Stevenson, Abigail
Zhang, Guangtao
Tang, Silin
author_sort Wu, Xingwen
collection PubMed
description The use of whole genome sequencing (WGS) data generated by the long-read sequencing platform Oxford Nanopore Technologies (ONT) has been shown to provide reliable results for Salmonella serotype prediction in a previous study. To further meet the needs of industry for accurate, rapid, and cost-efficient Salmonella confirmation and serotype classification, we evaluated the serotype prediction accuracy of using WGS data from multiplex ONT sequencing with three, four, five, seven, or ten Salmonella isolates (each isolate represented one Salmonella serotype) pooled in one R9.4.1 flow cell. Each multiplexing strategy was repeated with five flow cells, and the loaded samples were sequenced simultaneously in a GridION sequencer for 48 h. In silico serotype prediction was performed using both SeqSero2 (for raw reads and genome assemblies) and SISTR (for genome assemblies) software suites. An average of 10.63 Gbp of clean sequencing data was obtained per flow cell. We found that the unevenness of data yield among each multiplexed isolate was a major barrier for shortening sequencing time. Using genome assemblies, both SeqSero2 and SISTR accurately predicted all the multiplexed isolates under each multiplexing strategy when depth of genome coverage ≥50× for each isolate. We identified that cross-sample barcode assignment was a major cause of prediction errors when raw sequencing data were used for prediction. This study also demonstrated that, (i) sequence data generated by ONT multiplex sequencing can be used to simultaneously predict serotype for three to ten Salmonella isolates, (ii) with three to ten Salmonella isolates multiplexed, genome coverage at ≥50× per isolate was obtained within an average of 6 h of ONT multiplex sequencing, and (iii) with five isolates multiplexed, the cost per isolate might be reduced to 23% of that incurred with single ONT sequencing. This study is a starting point for future validation of multiplex ONT WGS as a cost-efficient and rapid Salmonella confirmation and serotype classification tool for the food industry.
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spelling pubmed-79878032021-03-25 Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing Wu, Xingwen Luo, Hao Xu, Feng Ge, Chongtao Li, Shaoting Deng, Xiangyu Wiedmann, Martin Baker, Robert C. Stevenson, Abigail Zhang, Guangtao Tang, Silin Front Microbiol Microbiology The use of whole genome sequencing (WGS) data generated by the long-read sequencing platform Oxford Nanopore Technologies (ONT) has been shown to provide reliable results for Salmonella serotype prediction in a previous study. To further meet the needs of industry for accurate, rapid, and cost-efficient Salmonella confirmation and serotype classification, we evaluated the serotype prediction accuracy of using WGS data from multiplex ONT sequencing with three, four, five, seven, or ten Salmonella isolates (each isolate represented one Salmonella serotype) pooled in one R9.4.1 flow cell. Each multiplexing strategy was repeated with five flow cells, and the loaded samples were sequenced simultaneously in a GridION sequencer for 48 h. In silico serotype prediction was performed using both SeqSero2 (for raw reads and genome assemblies) and SISTR (for genome assemblies) software suites. An average of 10.63 Gbp of clean sequencing data was obtained per flow cell. We found that the unevenness of data yield among each multiplexed isolate was a major barrier for shortening sequencing time. Using genome assemblies, both SeqSero2 and SISTR accurately predicted all the multiplexed isolates under each multiplexing strategy when depth of genome coverage ≥50× for each isolate. We identified that cross-sample barcode assignment was a major cause of prediction errors when raw sequencing data were used for prediction. This study also demonstrated that, (i) sequence data generated by ONT multiplex sequencing can be used to simultaneously predict serotype for three to ten Salmonella isolates, (ii) with three to ten Salmonella isolates multiplexed, genome coverage at ≥50× per isolate was obtained within an average of 6 h of ONT multiplex sequencing, and (iii) with five isolates multiplexed, the cost per isolate might be reduced to 23% of that incurred with single ONT sequencing. This study is a starting point for future validation of multiplex ONT WGS as a cost-efficient and rapid Salmonella confirmation and serotype classification tool for the food industry. Frontiers Media S.A. 2021-03-10 /pmc/articles/PMC7987803/ /pubmed/33776971 http://dx.doi.org/10.3389/fmicb.2021.637771 Text en Copyright © 2021 Wu, Luo, Xu, Ge, Li, Deng, Wiedmann, Baker, Stevenson, Zhang and Tang. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Microbiology
Wu, Xingwen
Luo, Hao
Xu, Feng
Ge, Chongtao
Li, Shaoting
Deng, Xiangyu
Wiedmann, Martin
Baker, Robert C.
Stevenson, Abigail
Zhang, Guangtao
Tang, Silin
Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title_full Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title_fullStr Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title_full_unstemmed Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title_short Evaluation of Salmonella Serotype Prediction With Multiplex Nanopore Sequencing
title_sort evaluation of salmonella serotype prediction with multiplex nanopore sequencing
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987803/
https://www.ncbi.nlm.nih.gov/pubmed/33776971
http://dx.doi.org/10.3389/fmicb.2021.637771
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