ArticleBMC genomics2024
Unraveling the impact of genome assembly on bacterial typing: a one health perspective.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From contiguity to accuracy: Validation-centered perspectives on bacterial genome assembly.Journal of microbiology (Seoul, Korea) · 2026Review
- Impact of sequencing read quality on whole-genome sequencing outcomes for foodborne pathogens using a BacWORK standardized analysis workflow.Bioinformatics advances · 2026Article
- Oxford Nanopore Technologies R10 sequencing enables accurate cgMLST-based bacterial outbreak investigation ofJournal of clinical microbiology · 2025Article
- Biocontrol Effect ofMicroorganisms · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the context of pathogen surveillance, it is crucial to ensure interoperability and harmonized data. Several surveillance systems are designed to compare bacteria and identify outbreak clusters based on core genome MultiLocus Sequence Typing (cgMLST). Among the different approaches available to generate bacterial cgMLST, our research used an assembly-based approach (chewBBACA tool).
methodsSimulations of short-read sequencing were conducted for 5 genomes of 27 pathogens of interest in animal, plant, and human health to evaluate the repeatability and reproducibility of cgMLST. Various quality parameters, such as read quality and depth of sequencing were applied, and several read simulations and genome assemblies were repeated using three tools: SPAdes, Unicycler and Shovill. In vitro sequencing were also used to evaluate assembly impact on cgMLST results, for six bacterial species: Bacillus thuringiensis, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, Vibrio parahaemolyticus and Xylella fastidiosa.
resultsThe results highlighted variability in cgMLST, which not only related to the assembly tools, but also induced by the intrinsic composition of the genomes themselves. This variability observed in simulated sequencing was further validated with real data for six of the bacterial pathogens studied.
conclusionThis highlights that the intrinsic genome composition affects assembly and resulting cgMLST profiles, and that variability in bioinformatics tools can induce a bias in cgMLST profiles. In conclusion, we propose that the completeness of cgMLST schemes should be considered when clustering strains.
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