Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation

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...

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Veröffentlicht in:Genes Jg. 12; H. 2; S. 275
Hauptverfasser: Barretto, Caroline, Rincón, Cristian, Portmann, Anne-Catherine, Ngom-Bru, Catherine
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 15.02.2021
MDPI
Schlagworte:
Bioinformatics
Computational Biology
computer software
Data Analysis
Data integrity
Factories
Food contamination & poisoning
Food industry
Food Industry - trends
Food Microbiology - trends
Food safety
Food sources
Genome, Bacterial - genetics
Genomes
Humans
Laboratories
Metadata
Open source software
Opinion
Pathogens
Quality control
Servers
Software
Standardization
Surveillance
Trouble shooting
Whole Genome Sequencing
food safety
whole genome sequencing
data quality
metadata
data analysis
workflow
bioinformatics
food industry
ISSN:2073-4425, 2073-4425
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Zusammenfassung: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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ISSN:2073-4425
2073-4425
DOI:10.3390/genes12020275