Evidence map›Paper›PMID 27559074›Full record

ReviewClinical microbiology reviews2016

Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing.

J Ronholm, Neda Nasheri, Nicholas Petronella, Franco Pagotto

Abstract readReview
In one paragraph

Review in Clinical microbiology reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed.

  1. Article
  2. Article
  3. Genomic epidemiology ofProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Article
  6. Genomic perspectives on foodborne illness.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Antibiotics (Basel, Switzerland) · 2024
    Article
  8. Article
  9. Article
  10. Genetic Characterization ofFoods (Basel, Switzerland) · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Molecular source attribution.PLoS computational biology · 2022
    Article
  16. ClinicalJournal of fungi (Basel, Switzerland) · 2022
    Review
  17. Review
  18. Review
  19. Article
  20. Article

2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

J RonholmBureau of Microbial Hazards, Food Directorate, Health Canada, Ottawa, ON, Canada jen.ronholm@gmail.com.
Neda NasheriBureau of Microbial Hazards, Food Directorate, Health Canada, Ottawa, ON, Canada.
Nicholas PetronellaBiostatistics and Modelling Division, Bureau of Food Surveillance and Science Integration, Food Directorate, Health Canada, Ottawa, ON, Canada.
Franco PagottoBureau of Microbial Hazards, Food Directorate, Health Canada, Ottawa, ON, Canada Listeriosis Reference Centre, Bureau of Microbial Hazards, Food Directorate, Health Canada, Ottawa, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Disease OutbreaksFood MicrobiologyFood SafetyFoodborne DiseasesGenomicsHumansMolecular Epidemiology

Identifiers

PMID27559074
PMCPMC5010751

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.