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Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe

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...

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Autores principales: Hunt, Martin, Bradley, Phelim, Lapierre, Simon Grandjean, Heys, Simon, Thomsit, Mark, Hall, Michael B., Malone, Kerri M., Wintringer, Penelope, Walker, Timothy M., Cirillo, Daniela M., Comas, Iñaki, Farhat, Maha R., Fowler, Phillip, Gardy, Jennifer, Ismail, Nazir, Kohl, Thomas A., Mathys, Vanessa, Merker, Matthias, Niemann, Stefan, Omar, Shaheed Vally, Sintchenko, Vitali, Smith, Grace, van Soolingen, Dick, Supply, Philip, Tahseen, Sabira, Wilcox, Mark, Arandjelovic, Irena, Peto, Tim E. A., Crook, Derrick W., Iqbal, Zamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004237/
https://www.ncbi.nlm.nih.gov/pubmed/32055708
http://dx.doi.org/10.12688/wellcomeopenres.15603.1
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author Hunt, Martin
Bradley, Phelim
Lapierre, Simon Grandjean
Heys, Simon
Thomsit, Mark
Hall, Michael B.
Malone, Kerri M.
Wintringer, Penelope
Walker, Timothy M.
Cirillo, Daniela M.
Comas, Iñaki
Farhat, Maha R.
Fowler, Phillip
Gardy, Jennifer
Ismail, Nazir
Kohl, Thomas A.
Mathys, Vanessa
Merker, Matthias
Niemann, Stefan
Omar, Shaheed Vally
Sintchenko, Vitali
Smith, Grace
van Soolingen, Dick
Supply, Philip
Tahseen, Sabira
Wilcox, Mark
Arandjelovic, Irena
Peto, Tim E. A.
Crook, Derrick W.
Iqbal, Zamin
author_facet Hunt, Martin
Bradley, Phelim
Lapierre, Simon Grandjean
Heys, Simon
Thomsit, Mark
Hall, Michael B.
Malone, Kerri M.
Wintringer, Penelope
Walker, Timothy M.
Cirillo, Daniela M.
Comas, Iñaki
Farhat, Maha R.
Fowler, Phillip
Gardy, Jennifer
Ismail, Nazir
Kohl, Thomas A.
Mathys, Vanessa
Merker, Matthias
Niemann, Stefan
Omar, Shaheed Vally
Sintchenko, Vitali
Smith, Grace
van Soolingen, Dick
Supply, Philip
Tahseen, Sabira
Wilcox, Mark
Arandjelovic, Irena
Peto, Tim E. A.
Crook, Derrick W.
Iqbal, Zamin
author_sort Hunt, Martin
collection PubMed
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.
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spelling pubmed-70042372020-02-12 Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe Hunt, Martin Bradley, Phelim Lapierre, Simon Grandjean Heys, Simon Thomsit, Mark Hall, Michael B. Malone, Kerri M. Wintringer, Penelope Walker, Timothy M. Cirillo, Daniela M. Comas, Iñaki Farhat, Maha R. Fowler, Phillip Gardy, Jennifer Ismail, Nazir Kohl, Thomas A. Mathys, Vanessa Merker, Matthias Niemann, Stefan Omar, Shaheed Vally Sintchenko, Vitali Smith, Grace van Soolingen, Dick Supply, Philip Tahseen, Sabira Wilcox, Mark Arandjelovic, Irena Peto, Tim E. A. Crook, Derrick W. Iqbal, Zamin Wellcome Open Res Software Tool Article 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. F1000 Research Limited 2019-12-02 /pmc/articles/PMC7004237/ /pubmed/32055708 http://dx.doi.org/10.12688/wellcomeopenres.15603.1 Text en Copyright: © 2019 Hunt M et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Tool Article
Hunt, Martin
Bradley, Phelim
Lapierre, Simon Grandjean
Heys, Simon
Thomsit, Mark
Hall, Michael B.
Malone, Kerri M.
Wintringer, Penelope
Walker, Timothy M.
Cirillo, Daniela M.
Comas, Iñaki
Farhat, Maha R.
Fowler, Phillip
Gardy, Jennifer
Ismail, Nazir
Kohl, Thomas A.
Mathys, Vanessa
Merker, Matthias
Niemann, Stefan
Omar, Shaheed Vally
Sintchenko, Vitali
Smith, Grace
van Soolingen, Dick
Supply, Philip
Tahseen, Sabira
Wilcox, Mark
Arandjelovic, Irena
Peto, Tim E. A.
Crook, Derrick W.
Iqbal, Zamin
Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title_full Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title_fullStr Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title_full_unstemmed Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title_short Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
title_sort antibiotic resistance prediction for mycobacterium tuberculosis from genome sequence data with mykrobe
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004237/
https://www.ncbi.nlm.nih.gov/pubmed/32055708
http://dx.doi.org/10.12688/wellcomeopenres.15603.1
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