Abstract:
: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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Bradley P,Gordon NC,Walker TM,Dunn L,Heys S,Huang B,Earle S,Pankhurst LJ,Anson L,de Cesare M,Piazza P,Votintseva AA,Golubchik T,Wilson DJ,Wyllie DH,Diel R,Niemann S,Feuerriegel S,Kohl TA,Ismail N,Omar SV,Smith Edoi
10.1038/ncomms10063subject
Has Abstractpub_date
2015-12-21 00:00:00pages
10063issn
2041-1723pii
ncomms10063journal_volume
6pub_type
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