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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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
Issue number1
Publication statusPublished - 1 Dec 2020

Bibliographical note

Funding Information:
As part of the COMPARE project, this work received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 643476. COMPARE is a Horizon 2020 EU project with the intention to speed up the detection of and response to disease outbreaks among humans and animals worldwide through the use of new genome technology. The aim is to reduce the impact and cost of disease outbreaks. We would also like to acknowledge Philipp Kirstahler from DTU Food for developing a script to retrieve sequences from dcc_Vivaldi to institution-based servers.

Publisher Copyright:
© 2020, The Author(s).


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