ArticleMicrobial genomics2020
AB_SA: Accessory genes-Based Source Attribution - tracing the source of
Article in Microbial genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Pangenome dynamics and population structure of the zoonotic pathogen Salmonella enterica serotype Hadar.Nature communications · 2026Article
- Article
- 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
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
- WGS-Based Lineage and Antimicrobial Resistance Pattern ofAntibiotics (Basel, Switzerland) · 2022Article
- Bayesian Source Attribution ofFrontiers in microbiology · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The partitioning of pathogenic strains isolated in environmental or human cases to their sources is challenging. The pathogens usually colonize multiple animal hosts, including livestock, which contaminate the food-production chain and the environment (e.g. soil and water), posing an additional public-health burden and major challenges in the identification of the source. Genomic data opens up new opportunities for the development of statistical models aiming to indicate the likely source of pathogen contamination. Here, we propose a computationally fast and efficient multinomial logistic regression source-attribution classifier to predict the animal source of bacterial isolates based on 'source-enriched' loci extracted from the accessory-genome profiles of a pangenomic dataset. Depending on the accuracy of the model's self-attribution step, the modeller selects the number of candidate accessory genes that best fit the model for calculating the likelihood of (source) category membership. The Accessory genes-Based Source Attribution (AB_SA) method was applied to a dataset of strains of
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