SnapperDB is implemented as a python application under the open source BSD license. All code and user guides are available at https://github.com/phe-bioinformatics/snapperdb. Reference genomes and SnapperDB configs are available at https://github.com/phe-bioinformatics/snapperdb_references.

In April 2015, Public Health England implemented whole genome sequencing (WGS) as a routine typing tool for public health surveillance of Salmonella, adopting a multilocus sequence typing (MLST) approach as a replacement for traditional serotyping. The WGS derived sequence type (ST) was compared to the phenotypic serotype for 6,887 isolates of S. enterica subspecies I, and of these, 6,616 (96%) were concordant. Of the 4% (n = 271) of isolates of subspecies I exhibiting a mismatch, 119 were due to a process error in the laboratory, 26 were likely caused by the serotype designation in the MLST database being incorrect and 126 occurred when two different serovars belonged to the same ST. The population structure of S. enterica subspecies II–IV differs markedly from that of subspecies I and, based on current data, defining the serovar from the clonal complex may be less appropriate for the classification of this group. Novel sequence types that were not present in the MLST database were identified in 8.6% of the total number of samples tested (including S. enterica subspecies I–IV and S. bongori) and these 654 isolates belonged to 326 novel STs. For S. enterica subspecies I, WGS MLST derived serotyping is a high throughput, accurate, robust, reliable typing method, well suited to routine public health surveillance. The combined output of ST and serovar supports the maintenance of traditional serovar nomenclature while providing additional insight on the true phylogenetic relationship between isolates.

This report presents the results of the project "Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 3, the comparison of isolates from different compartments along the food chain, and from humans using whole genome sequencing (WGS) analysis". The main objective was to compare L. monocytogenes isolates collected in the EU from ready-to-eat (RTE) foods, compartments along the food chain and from human cases by the use of WGS. A total of 1,143 L. monocytogenes isolates were selected for the study, including 333 human clinical isolates and 810 isolates from the food chain. The isolates were whole genome sequenced. The phylogeny showed a clear delineation between L. monocytogenes lineages and between clonal complexes within lineages. A range of typing methods were applied to the sequence data, providing the framework to answer questions on genetic diversity and epidemiological relationships. Retrospective analysis of nine outbreaks showed that WGS is a powerful tool in national and international outbreak investigations as WGS can accurately rule isolates in or out of outbreaks. Source attribution models showed bovine reservoir to be the main source of human disease although other sources also contributed and generally confidence intervals were high. Numerous consistent genetic linkages between a priori unlinked strains were identified, some of which involved isolates from multiple countries. The presence of putative markers conferring the potential to survive/multiply in the food chain and/or cause disease in humans was explored by detecting the presence of putative virulence genes, AMR genes and factors conferring the ability to persist in the food processing chain. This study has demonstrated one of the major benefits of WGS, which is the ability to address a wide range of questions including those on virulence, antimicrobial resistance, source attribution, surveillance and outbreak detection and investigation, in a single experiment.

We present the LiSEQ (Listeria SEQuencing) project, funded by the European Food Safety Agency (EFSA) to compare Listeria monocytogenes isolates collected in the European Union from ready-to-eat foods, compartments along the food chain (e.g. food-producing animals, food-processing environments) and humans. In this article, we report the molecular characterization of a selection of this data set employing whole-genome sequencing analysis. We present an overview of the strain diversity observed in different sampled sources, and characterize the isolates based on their virulence and resistance profile. We integrate into our analysis the global L. monocytogenes genome collection described by Moura and colleagues in 2016 to assess the representativeness of the LiSEQ collection in the context of known L. monocytogenes strain diversity.