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This repository contains data, code, and supplementary information for the manuscript
Brinda K, Callendrello A, Ma KC, MacFadden DR, Charalampous T, Lee RS, Cowley L, Wadsworth CB, Grad YH, Kucherov G, O’Grady J, Baym M, and Hanage WP: Rapid inference of antibiotic resistance and susceptibility by genomic neighbor typing, Nature Microbiology (in press), 2019.
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% for 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.
- RASE pipeline. RASE pipeline.
- RASE. Core RASE software package.
- RASE DB skeleton. Skeleton for creating novel databases.
- Sequencing data are available from http://doi.org/10.5281/zenodo.1405173. For the metagenomic experiments, only the filtered datasets (i.e., after removing the remaining human reads in silico) were made publicly available.
- Results: The outputs of the RASE pipeline for individual experiments from the paper are located in results/.
- Tables: Tables and supplementary tables are located in tables/.
- Figures: Figures and supplementary figures are located in figures/.
- Lab notebooks (sequencing of isolates (SP01-SP06) and additional MIC testing) are available from lab-notebooks/.
For information on quantification of the association of bacterial clones with antibiotic resistance using optimal lineage-to-resistance predictors, see the Supplementary Document 1. All code and data are located in optimal-predictors-lineages/.
MIT.