期刊
NATURE COMMUNICATIONS
卷 6, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/ncomms10063
关键词
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资金
- UK Clinical Research Collaboration (Wellcome Trust) [087646/Z/08/Z]
- UK Clinical Research Collaboration (Medical Research Council)
- UK Clinical Research Collaboration (National Institute for Health Research (NIHR)) [G0800778]
- NIHR Oxford Biomedical Research Centre
- NIHR Oxford Health Protection Research Unit on Healthcare Associated Infection and Anti-microbial Resistance
- EU FP7 Patho-Ngen-Trace
- Wellcome Trust Core Award Grant [090532/Z/09/Z]
- Wellcome Trust/Royal Society Sir Henry Dale Fellowships [102541/Z/13/Z, 101237/Z/13/Z]
- Wellcome Trust PhD studentship
- MRC funded prize studentship
- Wellcome Trust [100956/Z/13/Z]
- Wellcome Trust [102541/Z/13/Z] Funding Source: Wellcome Trust
- MRC [MR/J011398/1, G0800778, MR/K023985/1] Funding Source: UKRI
- Medical Research Council [1368769, G0800778, MR/J011398/1, MR/K023985/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10047, NF-SI-0508-10279, NF-SI-0513-10110, CL-2015-13-003] Funding Source: researchfish
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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