ArticleInternational journal of food microbiology2015
First step in using molecular data for microbial food safety risk assessment; hazard identification of Escherichia coli O157:H7 by coupling genomic data with in vitro adherence to human epithelial cells.
Article in International journal of food microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 41 citations in OpenAlex.
- Advancing microbial risk assessment: perspectives from the evolution of detection technologies.NPJ science of food · 2025Review
- Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.International journal of microbiology · 2024Review
- Improvement of quantitative microbiological risk assessment (QMRA) methodology through integration with gaenetic data.EFSA journal. European Food Safety Authority · 2023Article
- Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.Current research in food science · 2023Article
- Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms.EFSA journal. European Food Safety Authority · 2019Article
- Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.Risk analysis : an official publication of the Society for Risk Analysis · 2019Article
- LiSEQ - whole-genome sequencing of a cross-sectional survey of Listeria monocytogenes in ready-to-eat foods and human clinical cases in Europe.Microbial genomics · 2019Article
- Cumulative acquisition of pathogenicity islands has shaped virulence potential and contributed to the emergence of LEE-negative Shiga toxin-producing Escherichia coli strains.Emerging microbes & infections · 2019Article
- Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges.Frontiers in microbiology · 2019Review
- Phenotypic Prediction: LinkingFrontiers in microbiology · 2018Article
- Identification of Novel Biomarkers for Priority Serotypes of Shiga Toxin-ProducingFrontiers in microbiology · 2018Article
- Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing.Clinical microbiology reviews · 2016Review
- Impact ofCurrent opinion in food science · 2016Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
The potential for using whole genome sequencing (WGS) data in microbiological risk assessment (MRA) has been discussed on several occasions since the beginning of this century. Still, the proposed heuristic approaches have never been applied in a practical framework. This is due to the non-trivial problem of mapping microbial information consisting of thousands of loci onto a probabilistic scale for risks. The paradigm change for MRA involves translation of multidimensional microbial genotypic information to much reduced (integrated) phenotypic information and onwards to a single measure of human risk (i.e. probability of illness). In this paper a first approach in methodology development is described for the application of WGS data in MRA; this is supported by a practical example. That is, combining genetic data (single nucleotide polymorphisms; SNPs) for Shiga toxin-producing Escherichia coli (STEC) O157 with phenotypic data (in vitro adherence to epithelial cells as a proxy for virulence) leads to hazard identification in a Genome Wide Association Study (GWAS). This application revealed practical implications when using SNP data for MRA. These can be summarized by considering the following main issues: optimum sample size for valid inference on population level, correction for population structure, quantification and calibration of results, reproducibility of the analysis, links with epidemiological data, anchoring and integration of results into a systems biology approach for the translation of molecular studies to human health risk. Future developments in genetic data analysis for MRA should aim at resolving the mapping problem of processing genetic sequences to come to a quantitative description of risk. The development of a clustering scheme focusing on biologically relevant information of the microbe involved would be a useful approach in molecular data reduction for risk assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.