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

J. Nunez-Garcia; M. AbuOun; N. Storey; M. S. Brouwer; J. F. Delgado-Blas; S. S. Mo; N. Ellaby; K. T. Veldman; M. Haenni; P. Châtre; J. Y. Madec; J. A. Hammerl; C. Serna; M. Getino; R. La Ragione; T. Naas; A. A. Telke; P. Glaser; M. Sunde; B. Gonzalez-Zorn; M. J. Ellington; M. F. Anjum

doi:10.1038/s41598-022-16760-9

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

J. Nunez-Garcia
, M. AbuOun
, N. Storey
, M. S. Brouwer
, J. F. Delgado-Blas
, S. S. Mo
, N. Ellaby
, K. T. Veldman
, M. Haenni
, P. Châtre
, J. Y. Madec
, J. A. Hammerl
, C. Serna
, M. Getino
, R. La Ragione
, T. Naas
, A. A. Telke
, P. Glaser
, M. Sunde
, B. Gonzalez-Zorn

M. J. Ellington, M. F. Anjum^*

^*Corresponding author for this work

Genomics

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

17 Downloads (Pure)

Abstract

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.

Original language	English
Article number	14372
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-16760-9
Publication status	Published - 23 Aug 2022

Bibliographical note

Funding Information: Funding was from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830, in the ARDIG project within the One Health European Joint Programme; MFA also received funding from the Veterinary Medicines Directorate, UK, project VM0533.

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/.

Publisher Copyright: © Crown 2022

Citation: Nunez-Garcia, J., AbuOun, M., Storey, N. et al. Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance. Sci Rep 12, 14372 (2022).

DOI: https://doi.org/10.1038/s41598-022-16760-9

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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., ... Anjum, M. F. (2022). Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance. Scientific Reports, 12(1), Article 14372. https://doi.org/10.1038/s41598-022-16760-9

@article{eebde40888e646b78a53a056bf267b51,

title = "Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance",

abstract = "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.",

author = "J. Nunez-Garcia and M. AbuOun and N. Storey and Brouwer, \{M. S.\} and Delgado-Blas, \{J. F.\} and Mo, \{S. S.\} and N. Ellaby and Veldman, \{K. T.\} and M. Haenni and P. Ch{\^a}tre and Madec, \{J. Y.\} and Hammerl, \{J. A.\} and C. Serna and M. Getino and \{La Ragione\}, R. and T. Naas and Telke, \{A. A.\} and P. Glaser and M. Sunde and B. Gonzalez-Zorn and Ellington, \{M. J.\} and Anjum, \{M. F.\}",

note = "Funding Information: Funding was from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under Grant Agreement No 773830, in the ARDIG project within the One Health European Joint Programme; MFA also received funding from the Veterinary Medicines Directorate, UK, project VM0533. 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/. Publisher Copyright: {\textcopyright} Crown 2022 Citation: Nunez-Garcia, J., AbuOun, M., Storey, N. et al. Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance. Sci Rep 12, 14372 (2022). DOI: https://doi.org/10.1038/s41598-022-16760-9",

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Nunez-Garcia, J, AbuOun, M, Storey, N, Brouwer, MS, Delgado-Blas, JF, Mo, SS, Ellaby, N, Veldman, KT, Haenni, M, Châtre, P, Madec, JY, Hammerl, JA, Serna, C, Getino, M, La Ragione, R, Naas, T, Telke, AA, Glaser, P, Sunde, M, Gonzalez-Zorn, B, Ellington, MJ & Anjum, MF 2022, 'Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance', Scientific Reports, vol. 12, no. 1, 14372. https://doi.org/10.1038/s41598-022-16760-9

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T1 - Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance

AU - Nunez-Garcia, J.

AU - AbuOun, M.

AU - Storey, N.

AU - Brouwer, M. S.

AU - Delgado-Blas, J. F.

AU - Mo, S. S.

AU - Ellaby, N.

AU - Veldman, K. T.

AU - Haenni, M.

AU - Châtre, P.

AU - Madec, J. Y.

AU - Hammerl, J. A.

AU - Serna, C.

AU - Getino, M.

AU - La Ragione, R.

AU - Naas, T.

AU - Telke, A. A.

AU - Glaser, P.

AU - Sunde, M.

AU - Gonzalez-Zorn, B.

AU - Ellington, M. J.

AU - Anjum, M. F.

N1 - Funding Information: Funding was from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830, in the ARDIG project within the One Health European Joint Programme; MFA also received funding from the Veterinary Medicines Directorate, UK, project VM0533. 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/. Publisher Copyright: © Crown 2022 Citation: Nunez-Garcia, J., AbuOun, M., Storey, N. et al. Harmonisation of in-silico next-generation sequencing based methods for diagnostics and surveillance. Sci Rep 12, 14372 (2022). DOI: https://doi.org/10.1038/s41598-022-16760-9

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N2 - 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.

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

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