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Nat Commun. 2015 Dec 21;6:10063. doi: 10.1038/ncomms10063.

Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.

Author information

Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
Nuffield Department of Medicine, University of Oxford, Oxford OX1 1NF, UK.
Institute for Epidemiology, University Medical Hospital Schleswig-Holstein, Niemannsweg 11, 24105 Kiel, Germany.
Molecular and Experimental Mycobacteriology, Research Centre Borstel, Parkallee 1, 23845 Borstel, Germany.
German Centre for Infection Research, Partner Site Borstel, Parkallee 1, 23845 Borstel, Germany.
Centre for Tuberculosis, National Institute for Communicable Diseases, Private Bag X4 Sandringham, Johannesburg 2131, South Africa.
Department of Medical Microbiology, University of Pretoria, PO Box 667, Pretoria 0001, South Africa.
Regional Centre for Mycobacteriology, PHE Public Health Laboratory Birmingham. Heartlands Hospital, Bordesley Green East, Birmingham B9 5SS, UK.
Biomedical Research Centre, NIHR (National Institutes of Health Research) Oxford Biomedical Research Centre, Oxford OX3 7LE, UK.
National Infection Service, Public Health England, Wellington House, 133-155 Waterloo Road, London SE1 8UG, UK.


The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package ('Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.

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