Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data.Source code is available from http://katholt.github.io/srst2/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-014-0090-6) contains supplementary material, which is available to authorized users.

Wellcome Trust (grant number 098051).

Whole-genome sequencing (WGS) has emerged as a powerful tool for comparing bacterial isolates in outbreak detection and investigation. Here we demonstrate that WGS performed prospectively for national epidemiologic surveillance of Listeria monocytogenes has the capacity to be superior to our current approaches using pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), multilocus variable-number tandem-repeat analysis (MLVA), binary typing, and serotyping. Initially 423 L. monocytogenes isolates underwent WGS, and comparisons uncovered a diverse genetic population structure derived from three distinct lineages. MLST, binary typing, and serotyping results inferred in silico from the WGS data were highly concordant (>99%) with laboratory typing performed in parallel. However, WGS was able to identify distinct nested clusters within groups of isolates that were otherwise indistinguishable using our current typing methods. Routine WGS was then used for prospective epidemiologic surveillance on a further 97 L. monocytogenes isolates over a 12-month period, which provided a greater level of discrimination than that of conventional typing for inferring linkage to point source outbreaks. A risk-based alert system based on WGS similarity was used to inform epidemiologists required to act on the data. Our experience shows that WGS can be adopted for prospective L. monocytogenes surveillance and investigated for other pathogens relevant to public health.