• Jean Guard, PhD, DVM - USDA-ARS Athens, GA
  • Justin Vaughn, PhD - USDA-ARS Athens, GA
  • Brian Nadon, PhD - USDA-ARS Washington, DC
  • Brian Reeves, Google, Overland Park, KS

Lab Staff

  • Chris Gaby, PhD


Salmonellosis sickens 1.0-1.2 million people and has an estimated health and productivity impact of between $3.4 and 11.4 billion annually in the United States (Hoffmann and Anekwe, 2013) without even factoring in the costs of food recalls and other control measures. To reduce the impact of Salmonella infections, strains of the species are tracked by the US Centers for Disease Control to monitor outbreaks, the Food and Drug Administration for food safety and the Food Safety Inspection Service for monitoring the food supply system. These organizations face two key challenges. The CDC is receiving fewer salmonella samples to aid in tracking because more cases of salmonlellosis are being diagnosed with culture independent diagnostic tests (CIDT) that do not result in a salmonella strain being isolated. The FDA and CDC get samples of salmonella but need methods to predict how resistant these bacteria are to salmonella and sanitizers used to clean production facilities. We are developing a method to sequence DNA from a used CIDT kits without isolating salmonella and predict antibiotic and salmonella resistance from the DNA sequences of isolated salmonella.


FACT: Salmonella Typing and Phenotypic Prediction From Genomes and Metagenomes Using Population Genomics and Machine Learning
National Institute of Food and Agriculture
2019-09-01 to 2023-08-31
GRANT_NUMBER: 2019-67021-29924 Funding: $456,000 URL