About this Research Topic
Food safety inspection systems rely on sampling to verify the effectiveness of policies for reducing the risk of food illness. Routine sampling of raw and finished products, as well as surfaces (e.g. food processing equipment and environment), may allow detection and mitigation of problems before they lead to illnesses and outbreaks. For microbiological hazards, these systems have relied on qualitative and quantitative tests. The availability of robust and affordable next-generation sequencing technologies, methods for analyzing large whole-genome sequencing (WGS) datasets, and public databases for sharing data collected from food inspection and public health agencies, provide a new source of genetic information to help verify the effectiveness of food safety policies. Quantitative and predictive models built with WGS data can be employed to identify markers for emerging pathogen subtypes with higher virulence, expanded host range, and/or enhanced resistance.
Researchers can contribute to best practices for using WGS-based analytics to inform surveillance systems, for example, by identifying emerging pathogen subtypes, discovering novel biomarkers of pathogenicity and zoonotic potential, and monitoring for antimicrobial resistance. We welcome studies along these lines that integrate genomic analyses with food inspection systems. These studies would help guide food and environmental sampling, metadata collection, and the integration of supplementary (non-genomic) data like production records, use of safety interventions, and weather data, with genomic data to promote food safety.
We particularly seek studies that improve or add to existing quantitative methods for use of WGS data for food inspection, principally those that aid objective bio-marker discovery for use with predictive analytics. In contrast to the use of quantitative methods with human data, analysis of microbial data may require adjustments for non-linearity due to within-host genetic diversity and gene dosage effects. Lastly, we seek studies that guide the design of new inspection systems that could use WGS data, including sampling plans and techniques and methods for detecting pathogen subtypes to avoid surveillance bias.
Keywords: Genome wide association study (GWAS), Supervised Machine Learning, Unsupervised Machine Learning, predictive analytics, surveillance bias, biomarker discovery, pathogenicity, zoonotic potential, virulence, antimicrobial resistance
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