Whole genome sequencing (WGS), the sequencing of (nearly) complete genomes, has had an enormous impact on almost all fields of biology. However, its successful integration into routine public health practice has lagged behind, partially due to a lack of bioinformatics expertise and suitable bioinformatics workflows. This thesis aims to address the bottlenecks in the WGS data analysis and demonstrate its added value for routine public health surveillance of bacterial pathogens by developing and integrating data analysis methods that fulfill the requirements of a public health institute. Additionally, the developed methods were optimized for time-critical situations such as outbreaks, where providing a quick response facilitates efficient public health intervention, thereby minimizing the clinical and economic impact. Modern sequencing technologies generate large volumes of data, for which public health laboratories often do not have the necessary infrastructure nor expertise to analyse. Additionally, most laboratories operate under strict quality requirements, from which the bioinformatics analysis is not exempt. Therefore we developed and extensively validated bioinformatics workflows for several priority pathogens (Neisseria meningitidis, Shiga toxin-producing Escherichia coli, and Mycobacterium tuberculosis) to provide a more efficient and powerful alternative to the conventional molecular biology-based methods. A novel validation framework was constructed and optimized to validate the performance of bioinformatics assays, demonstrating generally high performance for the intended applications. ISO 15189 accreditation was obtained for the characterization of N. meningitidis based on the bioinformatics analysis of WGS data using this workflow, illustrating the feasibility of shifting towards WGS-based surveillance. User-friendly access to the workflows is provided to both internal and external scientists through web-accessible Galaxy instances. In emergencies, the sample-to-result time for WGS can be a limiting factor, as full runs on Illumina sequencers typically take several days to complete. Therefore, a real-time data analysis protocol for Illumina sequencing was implemented, enabling data analysis when the sequencing is ongoing, reducing total turnover time. The implementation of the protocol was coupled with an extensive performance evaluation to provide concrete guidelines for setting up time-optimized sequencing experiments (with and without the real-time analysis protocol) that maintain a predefined level of performance for the bioinformatics analysis. Afterwards, a method for pathogen surveillance and outbreak investigation that combines both core-genome multilocus sequence typing and single nucleotide polymorphism-typing was developed. This method was used to study the spread of ciprofloxacin-resistant Shigella within Belgium, successfully linking cases to domestic circulation and travel-related events. We also applied the developed data analysis methods to solve specific questions for various research projects. These projects cover many of the responsibilities of a public health institute, including phylogenomic investigation, surveillance of antimicrobial resistance, detection and characterization of genetically modified organisms, and real-time tracking of pathogen evolution. For some of these projects, pathogen-specific end-to-end isolate characterization workflows were also made available (i.e., Shigella spp, Staphylococcus aureus, Enterococcus faecium, and Enterococcus faecalis), facilitated by the modular design. The broad diversity of these applications highlights that WGS can provide an added value in many aspects of public health. Therefore, this thesis advocates for a centralized WGS-based surveillance system that can ultimately benefit the health of people, animals, and our environment.
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