A Practical Bioinformatics Workflow for Routine Analysis of Bacterial WGS Data

The use of whole-genome sequencing (WGS) for bacterial characterisation has increased substantially in the last decade. Its high throughput and decreasing cost have led to significant changes in outbreak investigations and surveillance of a wide variety of microbial pathogens. Despite the innumerable advantages of WGS, several drawbacks concerning data analysis and management, as well as a general lack of standardisation, hinder its integration in routine use. In this work, a bioinformatics workflow for (Illumina) WGS data is presented for bacterial characterisation including genome annotation, species identification, serotype prediction, antimicrobial resistance prediction, virulence-related genes and plasmid replicon detection, core-genome-based or single nucleotide polymorphism (SNP)-based phylogenetic clustering and sequence typing. Workflow was tested using a collection of 22 in-house sequences of Salmonella enterica isolates belonging to a local outbreak, coupled with a collection of 182 Salmonella genomes publicly available. No errors were reported during the execution period, and all genomes were analysed. The bioinformatics workflow can be tailored to other pathogens of interest and is freely available for academic and non-profit use as an uploadable file to the Galaxy platform.

Automated summary

The use of whole-genome sequencing (WGS) for bacterial characterisation has significantly increased in recent years. However, challenges in data analysis and management, as well as the lack of standardisation, hinder its routine use. This study presents a bioinformatics workflow for bacterial characterisation using WGS data, which includes genome annotation, species identification, serotype prediction, antimicrobial resistance prediction, virulence-related genes and plasmid replicon detection, phylogenetic clustering, and sequence typing.