ArticleEmerging infectious diseases2019
Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States.
Article in Emerging infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.Poultry science · 2026Article
- Genome-based source attribution using a One HealthMicrobial genomics · 2026Article
- GWAS and Machine Learning Screening of Genomic Determinants Underlying Host Adaptation in Swine and ChickenMicroorganisms · 2026Article
- Genomic epidemiology ofFrontiers in microbiology · 2026Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
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- Attribution of Salmonella enterica to Food Sources by Using Whole-Genome Sequencing Data.Emerging infectious diseases · 2025Article
- aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.Genome biology · 2025Article
- Article
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- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers inApplied and environmental microbiology · 2024Article
- A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing.Foodborne pathogens and disease · 2024Review
- Comparison of three source attribution methods applied to whole genome sequencing data of monophasic and biphasicFrontiers in microbiology · 2024Article
- Use of whole genome sequencing for surveillance and control of foodborne diseases:Frontiers in microbiology · 2024Review
- The advantage of intergenic regions as genomic features for machine-learning-based host attribution ofMicrobial genomics · 2023Article
- Thymol-Loaded Polymeric Nanoparticles Improve the Postharvest Microbiological Safety of Blueberries.Food technology and biotechnology · 2023Article
- Comparison of Source Attribution Methodologies for Human Campylobacteriosis.Pathogens (Basel, Switzerland) · 2023Article
- Rapid geographical source attribution ofeLife · 2023Article
- Development and validation of a random forest algorithm for source attribution of animal and humanFrontiers in microbiology · 2023Article
1 more citing papers are in PubMed but not listed here.
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17 authors.
Funding
Abstract
Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case-control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998-2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.
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