ArticleGenes2021
Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation.
Article in Genes, 2021. 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.
- Application of MSB selective enrichment followed by amplicon MinION sequencing for multipathogen detection in smoked salmon.NPJ science of food · 2025Article
- CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter.BMC bioinformatics · 2023Article
- Serotype Screening ofMicroorganisms · 2022Article
- Rapid Proteomic Characterization of Bacteriocin-ProducingInternational journal of molecular sciences · 2022Article
- Article
- Surveillance of Listeria monocytogenes: Early Detection, Population Dynamics, and Quasimetagenomic Sequencing during Selective Enrichment.Applied and environmental microbiology · 2021Article
- Genomic Surveillance of Foodborne Pathogens: Advances and Obstacles.Journal of public health management and practice : JPHMPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole genome sequencing (WGS) has arisen as a powerful tool to perform pathogen source tracking in the food industry thanks to several developments in recent years. However, the cost associated to this technology and the degree of expertise required to accurately process and understand the data has limited its adoption at a wider scale. Additionally, the time needed to obtain actionable information is often seen as an impairment for the application and use of the information generated via WGS. Ongoing work towards standardization of wet lab including sequencing protocols, following guidelines from the regulatory authorities and international standardization efforts make the technology more and more accessible. However, data analysis and results interpretation guidelines are still subject to initiatives coming from distinct groups and institutions. There are multiple bioinformatics software and pipelines developed to handle such information. Nevertheless, little consensus exists on a standard way to process the data and interpret the results. Here, we want to present the constraints we face in an industrial setting and the steps we consider necessary to obtain high quality data, reproducible results and a robust interpretation of the obtained information. All of this, in a time frame allowing for data-driven actions supporting factories and their needs.
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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.