Comprehensive assessment of the quality of Salmonella whole genome sequence data available in public sequence databases using the Salmonella in silico Typing Resource (SISTR)

Abstract

Public health and food safety institutions around the world are adopting whole genome sequencing (WGS) to replace conventional methods for characterizing Salmonella for use in surveillance and outbreak response. Falling costs and increased throughput of WGS have resulted in an explosion of data, but questions remain as to the reliability and robustness of the data. Due to the critical importance of serovar information to public health, it is essential to have reliable serovar assignments available for all of the Salmonella records. The current study used a systematic assessment and curation of all Salmonella in the sequence read archive (SRA) to assess the state of the data and their utility. A total of 67 758 genomes were assembled de novo and quality-assessed for their assembly metrics as well as species and serovar assignments. A total of 42 400 genomes passed all of the quality criteria but 30.16 % of genomes were deposited without serotype information. These data were used to compare the concordance of reported and predicted serovars for two in silico prediction tools, multi-locus sequence typing (MLST) and the Salmonella in silico Typing Resource (SISTR), which produced predictions that were fully concordant with 87.51 and 91.91 % of the tested isolates, respectively. Concordance of in silico predictions increased when serovar variants were grouped together, 89.25 % for MLST and 94.98 % for SISTR. This study represents the first large-scale validation of serovar information in public genomes and provides a large validated set of genomes, which can be used to benchmark new bioinformatics tools.

ConFindr: Rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data

“…Notably, intraspecies contamination was more prevalent than cross-species contamination. A recent assessment of 67758 publically-available Salmonella sequences determined that 1.87% of samples had cross-species contamination based on a read classification approach (Robertson et al, 2018). Prevalence of cross-species sequence contamination in public repositories is a known issue that has been described in a number of studies (Merchant et al, 2014;Mukherjee et al, 2015;Lee et al, 2017;Cornet et al, 2018).…”

“…The presence of contamination in WGS data is recognized as an important sequence quality issue (Merchant et al, 2014;Ballenghien et al, 2017;Robertson et al, 2018;Cornet et al, 2018). Introduction of contaminants can occur at many stages in the generation of bacterial sequence data.…”

“…Contiguity can be impacted by presence of contaminating sequencing reads, but also by factors such as the assembler used, length of the sequencing reads, presence of repeat regions, GC content and coverage (Lin et al, 2011;Jünemann et al, 2014;Souvorov et al, 2018). Contamination may be indicated by a highly fragmented assembly, or a genome size that is larger than expected (Robertson et al, 2018). However, establishment of appropriate cutoffs requires determination of acceptable ranges within a species, and atypical strains may fall outside of these limits.…”

Modern Salmonella subtyping methods in outbreak investigations

“…В тех случаях, когда установить антигенную формулу невозможно из-за неполноты данных WGS, платформа SISTR выбирает наиболее вероятный серотип внутри кластера cgMLST. В настоящее время SISTR внедрена в систему здравоохранения Канады как замена классическому серотипированию [40,52].…”

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

“…IRIDA currently performs Salmonella serotype prediction with the Salmonella In Silico Typing Resource (SISTR), a validated bioinformatics platform for rapid in silico inference from draft Salmonella genome assemblies (34). SISTR performs highly-accurate serovar prediction based on genoserotyping through sequence analysis of the Salmonella O and H antigens, with additional refinement of predictions based on population structure context via cgMLST analysis and genomic distance calculation using MASH (9,34,35). Results generated by SISTR are then incorporated into the Sample metadata and can be conveniently viewed in a single table.…”