Abstract
The routine use of whole-genome sequencing (WGS) as part of enteric disease surveillance is substantially enhancing our ability to detect and investigate outbreaks and to monitor disease trends. At the same time, it is revealing as never before the vast complexity of microbial and human interactions that contribute to outbreak ecology. Since WGS analysis is primarily used to characterize and compare microbial genomes with the goal of addressing epidemiological questions, it must be interpreted in an epidemiological context. In this article, we identify common challenges and pitfalls encountered when interpreting sequence data in an enteric disease surveillance and investigation context, and explain how to address them.
Highlights
Introduction and TerminologyDetection and investigation of outbreaks through molecular-based surveillance of foodborne pathogens by pulsed-field gel electrophoresis (PFGE) has proven to be a highly cost-effective tool for food safety (Scharff et al, 2016)
We identify common challenges and pitfalls encountered when interpreting sequence data in an enteric disease surveillance and investigation context, and explain how to address them
Early hopes that whole-genome sequencing (WGS) would drastically simplify the identification of contaminated foods and tracking them to their sources have largely been replaced by the realization that WGS reveals the vast complexity of microbial interactions with humans, animals, plants, and the environment
Summary
Detection and investigation of outbreaks through molecular-based surveillance of foodborne pathogens by pulsed-field gel electrophoresis (PFGE) has proven to be a highly cost-effective tool for food safety (Scharff et al, 2016). There are no absolute boundaries of genetic distance in the common-use clone definition, the distance is informed by a range of ecological considerations, and is initially usually defined using organism-specific ruleof-thumb genetic distance cutoff values (e.g., alleles or SNPs) derived from outbreak surveillance data, as described earlier. These boundaries may widen or shrink as new epidemiological information becomes available to inform the ‘‘likeliness’’ of a common origin. The ecology of enteric outbreaks can be complex, and interpretation of WGS for this purpose requires understanding of underlying assumptions and limitations, the use of graphical representations such as phylogenetic trees and similarity matrices, implications of analysis method and quality metrics, and approaches to detecting and triaging clusters
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