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

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…

• WT
  Wellcome Trust

  Awards: 101237/Z/13/Z, 102541/Z/13/Z, 087646/Z/08/Z, 100956/Z/13/Z, 090532, 101237, 090532/Z/09/Z, 090532/Z/09/
• ON
  Oxford Nanopore Technologies
• UK
  United Kingdom Clinical Research Collaboration
• NI
  National Institute for Health Research Health Protection Research Unit
• NI
  National Institute for Health and Care Research

  Awards: G0800778, NIHR grant G0800778
• UO
  University of Oxford
• MR
  Medical Research Council

  Awards: MR/J011398/1, MR/K023985/1, G0800778, grant G0800778, EU FP7
• NO
  NIHR Oxford Biomedical Research Centre

  Award: 090532/Z/09/Z