ArticleFrontiers in microbiology2024
Comparison of three source attribution methods applied to whole genome sequencing data of monophasic and biphasic
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Methodologies for source attribution (SA) of foodborne illnesses comprise a rapidly expanding suite of techniques for estimating the most important source or sources of human infection. Recently, the increasing availability of whole genome sequencing (WGS) data for a wide range of bacterial strains has led to the development of novel SA methods. These techniques utilize the unique features of bacterial genomes adapted to different host types and hence offer increased resolution of the outputs. Comparative studies of different SA techniques reliant on WGS data are currently lacking. Here, we critically assessed and compared the outputs of three SA methods: a supervised classification random forest machine learning algorithm (RandomForest), an Accessory genes-Based Source Attribution method (AB_SA), and a Bayesian frequency matching method (Bayesian). Each technique was applied to the WGS data of a panel of 902 reservoir host and human monophasic and biphasic
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