Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing
Bibliographic record
Abstract
The epidemiological investigation of a foodborne outbreak, including identification of related cases, source attribution, and development of intervention strategies, relies heavily on the ability to subtype the etiological agent at a high enough resolution to differentiate related from nonrelated cases. Historically, several different molecular subtyping methods have been used for this purpose; however, emerging techniques, such as single nucleotide polymorphism (SNP)-based techniques, that use whole-genome sequencing (WGS) offer a resolution that was previously not possible. With WGS, unlike traditional subtyping methods that lack complete information, data can be used to elucidate phylogenetic relationships and disease-causing lineages can be tracked and monitored over time. The subtyping resolution and evolutionary context provided by WGS data allow investigators to connect related illnesses that would be missed by traditional techniques. The added advantage of data generated by WGS is that these data can also be used for secondary analyses, such as virulence gene detection, antibiotic resistance gene profiling, synteny comparisons, mobile genetic element identification, and geographic attribution. In addition, several software packages are now available to generate in silico results for traditional molecular subtyping methods from the whole-genome sequence, allowing for efficient comparison with historical databases. Metagenomic approaches using next-generation sequencing have also been successful in the detection of nonculturable foodborne pathogens. This review addresses state-of-the-art techniques in microbial WGS and analysis and then discusses how this technology can be used to help support food safety investigations. Retrospective outbreak investigations using WGS are presented to provide organism-specific examples of the benefits, and challenges, associated with WGS in comparison to traditional molecular subtyping techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".