Comparison of SNP-based subtyping workflows for bacterial isolates using WGS data, applied to Salmonella enterica serotype Typhimurium and serotype 1,4,[5],12:i:-

Abstract

Whole genome sequencing represents a promising new technology for subtyping of bacterial pathogens. Besides the technological advances which have pushed the approach forward, the last years have been marked by considerable evolution of the whole genome sequencing data analysis methods. Prior to application of the technology as a routine epidemiological typing tool, however, reliable and efficient data analysis strategies need to be identified among the wide variety of the emerged methodologies. In this work, we have compared three existing SNP-based subtyping workflows using a benchmark dataset of 32 Salmonella enterica subsp. enterica serovar Typhimurium and serovar 1,4,[5],12:i:- isolates including five isolates from a confirmed outbreak and three isolates obtained from the same patient at different time points. The analysis was carried out using the original (high-coverage) and a down-sampled (low-coverage) datasets and two different reference genomes. All three tested workflows, namely CSI Phylogeny-based workflow, CFSAN-based workflow and PHEnix-based workflow, were able to correctly group the confirmed outbreak isolates and isolates from the same patient with all combinations of reference genomes and datasets. However, the workflows differed strongly with respect to the SNP distances between isolates and sensitivity towards sequencing coverage, which could be linked to the specific data analysis strategies used therein. To demonstrate the effect of particular data analysis steps, several modifications of the existing workflows were also tested. This allowed us to propose data analysis schemes most suitable for routine SNP-based subtyping applied to S. Typhimurium and S. 1,4,[5],12:i:-. Results presented in this study illustrate the importance of using correct data analysis strategies and to define benchmark and fine-tune parameters applied within routine data analysis pipelines to obtain optimal results.

Next-generation sequencing based hospital outbreak investigation yields insight into Klebsiella aerogenes population structure and determinants of carbapenem resistance and virulence

“…Mapping, variant calling and phylogenetic analysis were performed by locally installed CFSAN SNP Pipeline v1.0.0 (40), an analysis workflow developed by the U.S Food and Drug Administration (FDA). CFSAN pipeline employs a 2-phase variant calling workflow and the ‘optimized’ version of the pipeline with criteria as applied by Saltykova et al (higher stringency in allele frequency thresholds and coverage) (41) was applied. In the first phase, variants were called based on mpileup function of SAMTools and mpileup2snp tool from VarScan (minimum average base quality=20, minimum read depth of coverage at site=12, minimum allele frequency=90%).…”

Complete microbial genomes for public health in Australia and Southwest Pacific

“…Instead, they represent numerous diverse clades, with many being endemic to particular regions or jurisdictions (17–23). This inherent pathogen population structure poses a challenge to a successful transition to genomics in public health labs because it can significantly reduce the resolution of phylogenomic analyses by affecting the identification of genetic variants (24). Thus, catalogues of complete genomes will only be effective in supporting a transition to genomics in public health labs if they are rich in endemic strains.…”

Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing: Neisseria meningitidis as a Proof of Concept

“…This is a second problem that slows down the adoption of WGS in routine, i.e., the absence of an established methodology for the evaluation and comparison of WGS data analysis schemes. The different implementations of the methods described above can produce different outputs depending on the applied data processing steps and settings (Lüth et al, 2018;Saltykova et al, 2018). Studies which determine the performance characteristics such as reproducibility and discriminatory power of the WGS subtyping pipelines similarly to the classical methods, or that use various metrics to measure similarity between the SNP distance matrices and phylogenies generated by the different data analysis workflows have only recently started to emerge (David et al, 2016;Henri et al, 2017;Katz et al, 2017;Pearce et al, 2018;Saltykova et al, 2018).…”

“…The different implementations of the methods described above can produce different outputs depending on the applied data processing steps and settings (Lüth et al, 2018;Saltykova et al, 2018). Studies which determine the performance characteristics such as reproducibility and discriminatory power of the WGS subtyping pipelines similarly to the classical methods, or that use various metrics to measure similarity between the SNP distance matrices and phylogenies generated by the different data analysis workflows have only recently started to emerge (David et al, 2016;Henri et al, 2017;Katz et al, 2017;Pearce et al, 2018;Saltykova et al, 2018). Other important features such as the stability of the data analysis workflows toward the characteristics of the input sequencing data, the effect of the reference genome used for subtyping on the output, or the suitability of a workflow for subtyping of isolates with a particular relatedness level, are currently rarely assessed.…”