Contributed by Bart Weimer Ph.D.
WGS is becoming the method of choice for food safety outbreak and surveillance. The US CDC, US FDA, and the EU CDC (ECDC) have all announced that they will transition from pervious typing methods and only use WGS in the coming year. The announcement of these agencies will have a dramatic impact on the food industry and their ability to monitor the food chain with well-accepted methods http://www.foodqualitynews.com/Lab-Technology/WGS-cost-and-time-comparable-to-current-typing-methods).
It is clear that approved methods are missing outbreaks and leave doubt as to the identity of isolated pathogens. This is also boiling over into the clinical detection realm. Use of NGS is also moving full steam ahead into metagenomics; however, use of classical bioinformatic tools are not providing much success to accurately and robustly detect pathogens that may result in regulatory or actionable information from metagenomes.
This transition now has enough steam to carry WGS and metagenomics into the coming years for food chain management. The only questions are how fast, how much information is needed to manage/detect/ID pathogens, and how will industry implement a tool that is not in use today, except on a very small scale?
Recently Deng et al. (2014; PMID: 25147968) demonstrated that WGS delineated identical ID’s based on PFGE. This enabled a post hoc epidemiological investigation that linked seawater isolates to new linages responsible for human disease. This finding coupled with the mountain of evidence that WGS provides will be undeniable for those who contribute pathogens into the human environment – whether that is food, soil, or the clinic. The time has arrived that WGS is democratized and all need to have the capability to conduct this work in their own grasp. WGS moved from the bench in 2012 to government implementation in 2015 – that is an incredibly short transition with the speed increasing. Use of real time sequencing, represented by Oxford Nanopore (but others are coming), this speed of big data in microbiology is upon the food industry.