Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

Nanna Sophia Mucha Munck*, Pimlapas Leekitcharoenphon, Eva Litrup, Rolf Sommer Kaas, Anika Meinen, Laurent Guillier, Yue Tang, Burkhard Malorny, Federica Palma, Maria Borowiak, Michèle Gourmelon, Sandra Simon, Sangeeta Banerji, Liljana Petrovska, Timothy J Dallman, Tine Hald

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

63 Downloads (Pure)

Abstract

Zoonotic Salmonella causes millions of human salmonellosis infections worldwide each year. Information about the source of the bacteria guides risk managers on control and preventive strategies. Source attribution is the effort to quantify the number of sporadic human cases of a specific illness to specific sources and animal reservoirs. Source attribution methods for Salmonella have so far been based on traditional wet-lab typing methods. With the change to whole genome sequencing there is a need to develop new methods for source attribution based on sequencing data. Four European datasets collected in Denmark (DK), Germany (DE), the United Kingdom (UK) and France (FR) are presented in this descriptor. The datasets contain sequenced samples of Salmonella Typhimurium and its monophasic variants isolated from human, food, animal and the environment. The objective of the datasets was either to attribute the human salmonellosis cases to animal reservoirs or to investigate contamination of the environment by attributing the environmental isolates to different animal reservoirs.
Original languageEnglish
Article number75
JournalScientific Data
Volume7
Issue number1
Number of pages12
ISSN2052-4463
DOIs
Publication statusPublished - 2020

Cite this

Munck, N. S. M., Leekitcharoenphon, P., Litrup, E., Kaas, R. S., Meinen, A., Guillier, L., Tang, Y., Malorny, B., Palma, F., Borowiak, M., Gourmelon, M., Simon, S., Banerji, S., Petrovska, L., Dallman, T. J., & Hald, T. (2020). Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods. Scientific Data, 7(1), [75]. https://doi.org/10.1038/s41597-020-0417-7