Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could impact patient treatment and outcomes. Here we present a method called ‘genomic neighbor typing’ for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both S. pneumoniae and N. gonorrhoeae . We implemented this with rapid k -mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in determination of resistance within ten minutes (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from isolates with a representative database) of sequencing starting, and for clinical metagenomic sputum samples (75%/100% for S. pneumoniae ), within four hours of sample collection. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

Rapid heuristic inference of antibiotic resistance and susceptibility by genomic neighbor typing