Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations]
Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Myk...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wellcome
2019-12-01
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| Series: | Wellcome Open Research |
| Online Access: | https://wellcomeopenresearch.org/articles/4-191/v1 |
| _version_ | 1828373282515058688 |
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| author | Martin Hunt Phelim Bradley Simon Grandjean Lapierre Simon Heys Mark Thomsit Michael B. Hall Kerri M. Malone Penelope Wintringer Timothy M. Walker Daniela M. Cirillo Iñaki Comas Maha R. Farhat Phillip Fowler Jennifer Gardy Nazir Ismail Thomas A. Kohl Vanessa Mathys Matthias Merker Stefan Niemann Shaheed Vally Omar Vitali Sintchenko Grace Smith Dick van Soolingen Philip Supply Sabira Tahseen Mark Wilcox Irena Arandjelovic Tim E. A. Peto Derrick W. Crook Zamin Iqbal |
| author_facet | Martin Hunt Phelim Bradley Simon Grandjean Lapierre Simon Heys Mark Thomsit Michael B. Hall Kerri M. Malone Penelope Wintringer Timothy M. Walker Daniela M. Cirillo Iñaki Comas Maha R. Farhat Phillip Fowler Jennifer Gardy Nazir Ismail Thomas A. Kohl Vanessa Mathys Matthias Merker Stefan Niemann Shaheed Vally Omar Vitali Sintchenko Grace Smith Dick van Soolingen Philip Supply Sabira Tahseen Mark Wilcox Irena Arandjelovic Tim E. A. Peto Derrick W. Crook Zamin Iqbal |
| author_sort | Martin Hunt |
| collection | DOAJ |
| description | Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. Here we present a new tool, Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates. Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data. We measure the ability of Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools. |
| first_indexed | 2024-04-14T07:15:30Z |
| format | Article |
| id | doaj.art-13ce9ad14cbc4783b7d765b9e0628345 |
| institution | Directory Open Access Journal |
| issn | 2398-502X |
| language | English |
| last_indexed | 2024-04-14T07:15:30Z |
| publishDate | 2019-12-01 |
| publisher | Wellcome |
| record_format | Article |
| series | Wellcome Open Research |
| spelling | doaj.art-13ce9ad14cbc4783b7d765b9e06283452022-12-22T02:06:18ZengWellcomeWellcome Open Research2398-502X2019-12-01410.12688/wellcomeopenres.15603.117090Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations]Martin Hunt0Phelim Bradley1Simon Grandjean Lapierre2Simon Heys3Mark Thomsit4Michael B. Hall5Kerri M. Malone6Penelope Wintringer7Timothy M. Walker8Daniela M. Cirillo9Iñaki Comas10Maha R. Farhat11Phillip Fowler12Jennifer Gardy13Nazir Ismail14Thomas A. Kohl15Vanessa Mathys16Matthias Merker17Stefan Niemann18Shaheed Vally Omar19Vitali Sintchenko20Grace Smith21Dick van Soolingen22Philip Supply23Sabira Tahseen24Mark Wilcox25Irena Arandjelovic26Tim E. A. Peto27Derrick W. Crook28Zamin Iqbal29European Bioinformatics Institute, Cambridge, UKEuropean Bioinformatics Institute, Cambridge, UKInfectiology & immunology department, Universite de Montreal Microbiology, Montreal, CanadaEuropean Bioinformatics Institute, Cambridge, UKEuropean Bioinformatics Institute, Cambridge, UKEuropean Bioinformatics Institute, Cambridge, UKEuropean Bioinformatics Institute, Cambridge, UKEuropean Bioinformatics Institute, Cambridge, UKOxford University Clinical Research Unit, Ho Chi Minh City, VietnamEmerging Bacterial Pathogens Unit, WHO collaborating Centre and TB Supranational Reference laboratory, IRCCS San Raffaele Scientific institute, Milan, ItalyCIBER in Epidemiology and Public Health, Madrid, SpainHarvard Medical School, Boston, USANuffield Department of Medicine, University of Oxford, Oxford, UKBill and Melinda Gates Foundation, Seattle, USANational Institute for Communicable Diseases (NICD), Johannesburg, South AfricaForschungszentrum Borstel, Leibniz Lungenzentrum, Borstel, GermanyUnit Bacterial Diseases Service, Infectious Diseases in Humans, Sciensano, Brussels, BelgiumForschungszentrum Borstel, Leibniz Lungenzentrum, Borstel, GermanyForschungszentrum Borstel, Leibniz Lungenzentrum, Borstel, GermanyNational Institute for Communicable Diseases (NICD), Johannesburg, South AfricaCentre for Infectious Diseases and Microbiology - Public Health, University of Sydney, Sydney, AustraliaNational Mycobacterial Reference Service, Public Health England Public Health Laboratory, Birmingham, UKNational Institute for Public Health and the Environment (RIVM), Bilthoven, The NetherlandsUniv. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d'Infection et d'Immunite de Lille, Lille, FranceNational TB Reference Laboratory, National TB control Program, Islamabad, PakistanLeeds Teaching Hospital NHS Trust, Leeds, UKFaculty of Medicine, Institute of Microbiology and Immunology, Belgrade, SerbiaNuffield Department of Medicine, University of Oxford, Oxford, UKNuffield Department of Medicine, University of Oxford, Oxford, UKEuropean Bioinformatics Institute, Cambridge, UKTwo billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. Here we present a new tool, Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates. Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data. We measure the ability of Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools.https://wellcomeopenresearch.org/articles/4-191/v1 |
| spellingShingle | Martin Hunt Phelim Bradley Simon Grandjean Lapierre Simon Heys Mark Thomsit Michael B. Hall Kerri M. Malone Penelope Wintringer Timothy M. Walker Daniela M. Cirillo Iñaki Comas Maha R. Farhat Phillip Fowler Jennifer Gardy Nazir Ismail Thomas A. Kohl Vanessa Mathys Matthias Merker Stefan Niemann Shaheed Vally Omar Vitali Sintchenko Grace Smith Dick van Soolingen Philip Supply Sabira Tahseen Mark Wilcox Irena Arandjelovic Tim E. A. Peto Derrick W. Crook Zamin Iqbal Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] Wellcome Open Research |
| title | Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_full | Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_fullStr | Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_full_unstemmed | Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_short | Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe [version 1; peer review: 2 approved, 1 approved with reservations] |
| title_sort | antibiotic resistance prediction for mycobacterium tuberculosis from genome sequence data with mykrobe version 1 peer review 2 approved 1 approved with reservations |
| url | https://wellcomeopenresearch.org/articles/4-191/v1 |
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