PT  - JOURNAL ARTICLE
AU  - Břinda, Karel
AU  - Callendrello, Alanna
AU  - Cowley, Lauren
AU  - Charalampous, Themoula
AU  - Lee, Robyn S
AU  - MacFadden, Derek R
AU  - Kucherov, Gregory
AU  - O’Grady, Justin
AU  - Baym, Michael
AU  - Hanage, William P
TI  - Lineage calling can identify antibiotic resistant clones within minutes
AID  - 10.1101/403204
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 403204
4099  - http://biorxiv.org/content/early/2018/08/29/403204.short
4100  - http://biorxiv.org/content/early/2018/08/29/403204.full
AB  - Surveillance of circulating drug resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, the results of surveillance may not be available on a timescale that is optimal for guiding patient treatment. Here we present a method for inferring characteristics of an unknown bacterial sample by identifying the presence of sequence variation across the genome that is linked to a phenotype of interest, in this case drug resistance. We demonstrate an implementation of this principle using sequence k-mer content, matched to a database of known genomes. We show this technique can be applied to data from an Oxford Nanopore device in real time and is capable of identifying the presence of a known resistant strain in 5 minutes, even from a complex metagenomic sample. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate diagnoses of resistant infections.