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, n¼470). For M. tuberculosis, our method predicts resistance with
sensitivity/specificity of 82.6%/98.5% (independent validation set, n¼1,609); 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.