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Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance

Improvements in cost and speed of next generation sequencing (NGS) have provided a new pathway for delivering disease diagnosis, molecular typing, and detection of antimicrobial resistance (AMR). Numerous published methods and protocols exist, but a lack of harmonisation has hampered meaningful comp...

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Autores principales: Nunez-Garcia, J., AbuOun, M., Storey, N., Brouwer, M. S., Delgado-Blas, J. F., Mo, S. S., Ellaby, N., Veldman, K. T., Haenni, M., Châtre, P., Madec, J. Y., Hammerl, J. A., Serna, C., Getino, M., La Ragione, R., Naas, T., Telke, A. A., Glaser, P., Sunde, M., Gonzalez-Zorn, B., Ellington, M. J., Anjum, M. F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396611/
https://www.ncbi.nlm.nih.gov/pubmed/35999234
http://dx.doi.org/10.1038/s41598-022-16760-9
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author Nunez-Garcia, J.
AbuOun, M.
Storey, N.
Brouwer, M. S.
Delgado-Blas, J. F.
Mo, S. S.
Ellaby, N.
Veldman, K. T.
Haenni, M.
Châtre, P.
Madec, J. Y.
Hammerl, J. A.
Serna, C.
Getino, M.
La Ragione, R.
Naas, T.
Telke, A. A.
Glaser, P.
Sunde, M.
Gonzalez-Zorn, B.
Ellington, M. J.
Anjum, M. F.
author_facet Nunez-Garcia, J.
AbuOun, M.
Storey, N.
Brouwer, M. S.
Delgado-Blas, J. F.
Mo, S. S.
Ellaby, N.
Veldman, K. T.
Haenni, M.
Châtre, P.
Madec, J. Y.
Hammerl, J. A.
Serna, C.
Getino, M.
La Ragione, R.
Naas, T.
Telke, A. A.
Glaser, P.
Sunde, M.
Gonzalez-Zorn, B.
Ellington, M. J.
Anjum, M. F.
author_sort Nunez-Garcia, J.
collection PubMed
description Improvements in cost and speed of next generation sequencing (NGS) have provided a new pathway for delivering disease diagnosis, molecular typing, and detection of antimicrobial resistance (AMR). Numerous published methods and protocols exist, but a lack of harmonisation has hampered meaningful comparisons between results produced by different methods/protocols vital for global genomic diagnostics and surveillance. As an exemplar, this study evaluated the sensitivity and specificity of five well-established in-silico AMR detection software where the genotype results produced from running a panel of 436 Escherichia coli were compared to their AMR phenotypes, with the latter used as gold-standard. The pipelines exploited previously known genotype–phenotype associations. No significant differences in software performance were observed. As a consequence, efforts to harmonise AMR predictions from sequence data should focus on: (1) establishing universal minimum to assess performance thresholds (e.g. a control isolate panel, minimum sensitivity/specificity thresholds); (2) standardising AMR gene identifiers in reference databases and gene nomenclature; (3) producing consistent genotype/phenotype correlations. The study also revealed limitations of in-silico technology on detecting resistance to certain antimicrobials due to lack of specific fine-tuning options in bioinformatics tool or a lack of representation of resistance mechanisms in reference databases. Lastly, we noted user friendliness of tools was also an important consideration. Therefore, our recommendations are timely for widespread standardisation of bioinformatics for genomic diagnostics and surveillance globally.
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spelling pubmed-93966112022-08-23 Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance Nunez-Garcia, J. AbuOun, M. Storey, N. Brouwer, M. S. Delgado-Blas, J. F. Mo, S. S. Ellaby, N. Veldman, K. T. Haenni, M. Châtre, P. Madec, J. Y. Hammerl, J. A. Serna, C. Getino, M. La Ragione, R. Naas, T. Telke, A. A. Glaser, P. Sunde, M. Gonzalez-Zorn, B. Ellington, M. J. Anjum, M. F. Sci Rep Article Improvements in cost and speed of next generation sequencing (NGS) have provided a new pathway for delivering disease diagnosis, molecular typing, and detection of antimicrobial resistance (AMR). Numerous published methods and protocols exist, but a lack of harmonisation has hampered meaningful comparisons between results produced by different methods/protocols vital for global genomic diagnostics and surveillance. As an exemplar, this study evaluated the sensitivity and specificity of five well-established in-silico AMR detection software where the genotype results produced from running a panel of 436 Escherichia coli were compared to their AMR phenotypes, with the latter used as gold-standard. The pipelines exploited previously known genotype–phenotype associations. No significant differences in software performance were observed. As a consequence, efforts to harmonise AMR predictions from sequence data should focus on: (1) establishing universal minimum to assess performance thresholds (e.g. a control isolate panel, minimum sensitivity/specificity thresholds); (2) standardising AMR gene identifiers in reference databases and gene nomenclature; (3) producing consistent genotype/phenotype correlations. The study also revealed limitations of in-silico technology on detecting resistance to certain antimicrobials due to lack of specific fine-tuning options in bioinformatics tool or a lack of representation of resistance mechanisms in reference databases. Lastly, we noted user friendliness of tools was also an important consideration. Therefore, our recommendations are timely for widespread standardisation of bioinformatics for genomic diagnostics and surveillance globally. Nature Publishing Group UK 2022-08-23 /pmc/articles/PMC9396611/ /pubmed/35999234 http://dx.doi.org/10.1038/s41598-022-16760-9 Text en © Crown 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Nunez-Garcia, J.
AbuOun, M.
Storey, N.
Brouwer, M. S.
Delgado-Blas, J. F.
Mo, S. S.
Ellaby, N.
Veldman, K. T.
Haenni, M.
Châtre, P.
Madec, J. Y.
Hammerl, J. A.
Serna, C.
Getino, M.
La Ragione, R.
Naas, T.
Telke, A. A.
Glaser, P.
Sunde, M.
Gonzalez-Zorn, B.
Ellington, M. J.
Anjum, M. F.
Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title_full Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title_fullStr Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title_full_unstemmed Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title_short Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
title_sort harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9396611/
https://www.ncbi.nlm.nih.gov/pubmed/35999234
http://dx.doi.org/10.1038/s41598-022-16760-9
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