FN Archimer Export Format
PT J
TI AB_SA: Accessory genes-Based Source Attribution – tracing the source of Salmonella enterica Typhimurium environmental strains
BT 
AF Guillier, Laurent
   Gourmelon, Michele
   Lozach, Solen
   Cadel-Six, Sabrina
   Vignaud, Marie-Léone
   Munck, Nanna
   Hald, Tine
   Palma, Federica
AS 1:1,2;2:3;3:3;4:2;5:2;6:4;7:4;8:2;
FF 1:;2:PDG-RBE-SGMM-LSEM;3:PDG-RBE-SGMM-LSEM;4:;5:;6:;7:;8:;
C1 Risk Assessment Department, ANSES, University of Paris-EST, Maisons-Alfort, France
   Laboratory for Food Safety, ANSES, University of Paris-EST, Maisons-Alfort, France
   RBE–SGMM, Health, Environment and Microbiology Laboratory, IFREMER, Plouzané, France
   Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
C2 ANSES, FRANCE
   ANSES, FRANCE
   IFREMER, FRANCE
   UNIV TECH DENMARK (DTU AQUA), DENMARK
SI BREST
SE PDG-RBE-SGMM-LSEM
IN WOS Ifremer UPR
   DOAJ
   copubli-france
   copubli-europe
IF 5.237
TC 7
UR https://archimer.ifremer.fr/doc/00624/73632/73072.pdf
   https://archimer.ifremer.fr/doc/00624/73632/73073.xlsx
LA English
DT Article
DE ;environmental contamination;multinomial logistic regression;pangenome-wide enrichment analysis;source attribution;Salmonella Typhimurium
AB 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 Salmonella enterica Typhimurium and its monophasic variant ( S . enterica 1,4,[5],12:i:-). The model was trained on 69 strains with known animal-source categories (i.e. poultry, ruminant and pig). The AB_SA method helped to identify 8 genes as predictors among the 2802 accessory genes. The self-attribution accuracy was 80 %. The AB_SA model was then able to classify 25 of the 29 S . enterica Typhimurium and S . enterica 1,4,[5],12:i:- isolates collected from the environment (considered to be of unknown source) into a specific category (i.e. animal source), with more than 85 % of probability. The AB_SA method herein described provides a user-friendly and valuable tool for performing source-attribution studies in only a few steps. AB_SA is written in R and freely available at https://github.com/lguillier/AB_SA. 
PY 2020
PD JUN
SO Microbial Genomics
SN 2057-5858
PU Microbiology Society
VL 6
IS 7
UT 000576755800008
DI 10.1099/mgen.0.000366
ID 73632
ER

EF