Comparison of three source attribution methods applied to whole genome sequencing data of monophasic and biphasic <i>Salmonella</i> Typhimurium isolates from the British Isles and Denmark.

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

SUBMITTER: Guzinski J 

PROVIDER: S-EPMC11602282 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature