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Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis

Phelim Bradley, N. Claire Gordon, Timothy M. Walker, Laura Dunn, Simon Heys, Bill Huang, Sarah Earle, Louise J. Pankhurst, Luke Anson, Mariateresa de Cesare, Paolo Piazza, Antonina A. Votintseva, Tanya Golubchik, Daniel J. Wilson, David H. Wyllie, Roland Diel, Stefan Niemann, Silke Feuerriegel, Thomas A. Kohl, Nazir Ismail, Shaheed V. Omar, E. Grace Smith, David Buck, Gil McVean, A. Sarah Walker, Tim E.A. Peto, Derrick W. Crook, View ORCID ProfileZamin Iqbal
doi: https://doi.org/10.1101/018564
Phelim Bradley
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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N. Claire Gordon
2Nuffield Department of Medicine, University of Oxford, UK
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Timothy M. Walker
2Nuffield Department of Medicine, University of Oxford, UK
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Laura Dunn
2Nuffield Department of Medicine, University of Oxford, UK
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Simon Heys
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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Bill Huang
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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Sarah Earle
2Nuffield Department of Medicine, University of Oxford, UK
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Louise J. Pankhurst
2Nuffield Department of Medicine, University of Oxford, UK
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Luke Anson
2Nuffield Department of Medicine, University of Oxford, UK
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Mariateresa de Cesare
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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Paolo Piazza
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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Antonina A. Votintseva
2Nuffield Department of Medicine, University of Oxford, UK
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Tanya Golubchik
2Nuffield Department of Medicine, University of Oxford, UK
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Daniel J. Wilson
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
2Nuffield Department of Medicine, University of Oxford, UK
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David H. Wyllie
2Nuffield Department of Medicine, University of Oxford, UK
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Roland Diel
5Institute for Epidemiology, University Medical Hospital Schleswig-Holstein, Kiel, Germany
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Stefan Niemann
6Research Centre Borstel, Borstel, Germany
7German Centre for Infection Research, Partner Site Borstel, Borstel, Germany
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Silke Feuerriegel
6Research Centre Borstel, Borstel, Germany
7German Centre for Infection Research, Partner Site Borstel, Borstel, Germany
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Thomas A. Kohl
6Research Centre Borstel, Borstel, Germany
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Nazir Ismail
8National Institute for Communicable Diseases, Johannesberg, South Africa
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Shaheed V. Omar
8National Institute for Communicable Diseases, Johannesberg, South Africa
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E. Grace Smith
4Public Health England, UK
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David Buck
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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Gil McVean
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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A. Sarah Walker
2Nuffield Department of Medicine, University of Oxford, UK
3NIHR (National Institutes of Health Research) Oxford Biomedical Research Centre, Oxford, UK
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Tim E.A. Peto
2Nuffield Department of Medicine, University of Oxford, UK
3NIHR (National Institutes of Health Research) Oxford Biomedical Research Centre, Oxford, UK
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Derrick W. Crook
2Nuffield Department of Medicine, University of Oxford, UK
3NIHR (National Institutes of Health Research) Oxford Biomedical Research Centre, Oxford, UK
4Public Health England, UK
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Zamin Iqbal
1Wellcome Trust Centre for Human Genetics, University of Oxford, UK
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  • ORCID record for Zamin Iqbal
  • For correspondence: zam@well.ox.ac.uk
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Abstract

Rapid and accurate detection of antibiotic resistance in pathogens is an urgent need, affecting both patient care and population-scale control. Microbial genome sequencing promises much, but many barriers exist to its routine deployment. Here, we address these challenges, using a de Bruijn graph comparison of clinical isolate and curated knowledge-base to identify species and predict resistance profile, including minor populations. This is implemented in a package, Mykrobe predictor, for S. aureus and M. tuberculosis, running in under three minutes on a laptop from raw data. For S. aureus, we train and validate in 495/471 samples respectively, finding error rates comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.3%/99.5% across 12 drugs. For M. tuberculosis, we identify species and predict resistance with specificity of 98.5% (training/validating on 1920/1609 samples). Sensitivity of 82.6% is limited by current understanding of genetic mechanisms. Finally, we demonstrate feasibility of an emerging single-molecule sequencing technique.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 26, 2015.
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Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis
Phelim Bradley, N. Claire Gordon, Timothy M. Walker, Laura Dunn, Simon Heys, Bill Huang, Sarah Earle, Louise J. Pankhurst, Luke Anson, Mariateresa de Cesare, Paolo Piazza, Antonina A. Votintseva, Tanya Golubchik, Daniel J. Wilson, David H. Wyllie, Roland Diel, Stefan Niemann, Silke Feuerriegel, Thomas A. Kohl, Nazir Ismail, Shaheed V. Omar, E. Grace Smith, David Buck, Gil McVean, A. Sarah Walker, Tim E.A. Peto, Derrick W. Crook, Zamin Iqbal
bioRxiv 018564; doi: https://doi.org/10.1101/018564
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Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis
Phelim Bradley, N. Claire Gordon, Timothy M. Walker, Laura Dunn, Simon Heys, Bill Huang, Sarah Earle, Louise J. Pankhurst, Luke Anson, Mariateresa de Cesare, Paolo Piazza, Antonina A. Votintseva, Tanya Golubchik, Daniel J. Wilson, David H. Wyllie, Roland Diel, Stefan Niemann, Silke Feuerriegel, Thomas A. Kohl, Nazir Ismail, Shaheed V. Omar, E. Grace Smith, David Buck, Gil McVean, A. Sarah Walker, Tim E.A. Peto, Derrick W. Crook, Zamin Iqbal
bioRxiv 018564; doi: https://doi.org/10.1101/018564

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