ArticleJournal of clinical microbiology2016
Exploiting Bacterial Whole-Genome Sequencing Data for Evaluation of Diagnostic Assays: Campylobacter Species Identification as a Case Study.
Article in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Whole genome sequencing in animal health: applications, challenges, and future directions.BMC veterinary research · 2026Review
- Utilizing large and diverse bacterial genome datasets to improve the detection and identification ofMicrobial genomics · 2025Article
- Economic evaluation of whole genome sequencing for pathogen identification and surveillance - results of case studies in Europe and the Americas 2016 to 2019.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2021Article
- Whole genome sequencing reveals extended natural transformation in Campylobacter impacting diagnostics and the pathogens adaptive potential.Scientific reports · 2020Article
- Developments in Rapid Detection Methods for the Detection of FoodborneFrontiers in microbiology · 2018Review
- Molecular Assay Validation Using Genomic Sequence Databases.Journal of clinical microbiology · 2016Article
- Glucose Metabolism via the Entner-Doudoroff Pathway inFrontiers in microbiology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The application of whole-genome sequencing (WGS) to problems in clinical microbiology has had a major impact on the field. Clinical laboratories are now using WGS for pathogen identification, antimicrobial susceptibility testing, and epidemiological typing. WGS data also represent a valuable resource for the development and evaluation of molecular diagnostic assays, which continue to play an important role in clinical microbiology. To demonstrate this application of WGS, this study used publicly available genomic data to evaluate a duplex real-time PCR (RT-PCR) assay that targets mapA and ceuE for the detection of Campylobacter jejuni and Campylobacter coli, leading global causes of bacterial gastroenteritis. In silico analyses of mapA and ceuE primer and probe sequences from 1,713 genetically diverse C. jejuni and C. coli genomes, supported by RT-PCR testing, indicated that the assay was robust, with 1,707 (99.7%) isolates correctly identified. The high specificity of the mapA-ceuE assay was the result of interspecies diversity and intraspecies conservation of the target genes in C. jejuni and C. coli Rare instances of a lack of specificity among C. coli isolates were due to introgression in mapA or sequence diversity in ceuE The results of this study illustrate how WGS can be exploited to evaluate molecular diagnostic assays by using publicly available data, online databases, and open-source software.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.