Whole Genome Sequencing (WGS) is a game-changer when it comes to food safety. With whole-genome sequencing, we can fundamentally shift how we approach detection, traceback, investigations into root cause, detection technology use/development, and microbial understanding of pathogenesis. In short, insight into the genetic code has ushered in a new era of science and understanding. When you pair these disciplines with machine learning and other AI approaches, we have yet to even scrape the surface of microbial understanding and applied solutions.
While the food industry has become accustomed to seeing references to WGS, it has most frequently been encountered related to strain tracking for both environmental isolates (e.g., facility monitoring data) and outbreak investigations. It seems unnecessary to say it, but WGS technology offers us so much more, and it is worth digging a little deeper to see beyond the outbreak tracking applications.
One promising application of sequencing was recently published in the Journal of Infection and covered online in Food Safety & Quality Magazine. In this study, performed as part of the DISCOVER research project of the One Health European Joint Program, 10,000 Salmonella genomes were analyzed from human, animal, and environmental sources across Europe (Denmark, England and Wales, France, Ireland, the Netherlands, Poland, Portugal, Spain, and Sweden). The researchers used core genome multi-locus sequencing (cgMLST), a whole genome–based method that compares allelic differences across hundreds of conserved core genes to evaluate genetic relatedness and can be used with source information, epidemiology information, etc. to predict source attribution.
The international group of researchers focused on five frequently reported Salmonella serovars in the European Union and combined cgMLST and a supervised machine-learning approach (a Random Forest classifier) to analyze a dataset of human illness-related strains to try to classify the source attribution. This is important information since it offers a way to better understand contamination pathways and where efforts are needed to be able to more pointedly reduce the burden of foodborne illness.
Additionally, besides source attribution (pigs, cattle, reptiles, canine, poultry, etc.), this study found that isolates are moving across country borders. This is also important since it further emphasizes that food safety solutions need to be a collaborative effort, engaging broad stakeholder groups since the risk transcend borders and regulatory authority. Microbes simply don’t care about our state, country, or regulatory agency barriers, and our management practices need to follow suit.
Core-genome multilocus sequence typing (cgMLST) paired with machine-learning used in this study is just another tool in our sequencing/analysis toolbox. Approaches like this one are gaining both momentum and accuracy to be able to dive into larger trends, patterns of movement, selective pressures impacting biological function, and pathogenesis for foodborne pathogens.
Long story short, there’s a lot to keep track of in the sequencing and AI space – far beyond outbreak strain tracking. With a more global view of foodborne pathogens, we can, and are, connecting the dots and relationships that have previously eluded us. With this improved understanding of patterns of origin, environmental fitness, etc., we can also work to fine-tune approaches to be truly risk-based.
It’s definitely a field worth paying attention to—it’s most certainly going to expose and inform our world from the nucleotide up.
Check out these sources to learn more about the study:
Teunis, G., et.al. 2025. Attributable sources of the five most prevalent non-typhoidal Salmonella serovars across ten European countries. J.of Infection. Vol 91. Issue 5. Available here: https://www.journalofinfection.com/article/S0163-4453(25)00232-4/fulltext
Bailee Henderson. Machine Learning Analysis Shows Significance of Cross-Border Salmonella Transmission in Europe. Food Safety & Quality Magazine. Accessed December 12th, 2025. Available here: https://www.food-safety.com/articles/10968-machine-learning-analysis-shows-significance-of-cross-border-salmonella-transmission-in-europe