Abstract Motivation Whole-genome sequencing (WGS) is now routinely used for surveillance and outbreak investigations of foodborne pathogens. However, the reliability of downstream analyses strongly depends on sequencing read quality, including depth of coverage and the presence of contamination. Systematic evaluation of how these factors affect WGS outputs remains essential to ensure robust, reproducible, and interpretable results. Results We evaluated the impact of sequencing depth and read quality on WGS-derived analyses of foodborne pathogens using an automated, reproducible workflow. Artificial Illumina® paired-end datasets were generated for 11 bacterial pathogens with varying depths of coverage (25×–500×) and controlled quality alterations, including intra- and inter-species contamination. Low sequencing depth resulted in fragmented and incomplete genome assemblies, leading to reduced performance in downstream typing analyses. While contamination impacted assembly fragmentation, it did not compromise completeness. However, it caused significant errors in both serotyping and cgMLST analyses. Application to real outbreak datasets showed that the introduction of as little as 5–10% contamination altered a Salmonella enterica strain clustering by increasing cgMLST allelic distances with epidemiologically related isolates. These findings highlight that even low-level contamination can significantly compromise bacterial typing and outbreak interpretation. The BacWORK workflow was used to support this evaluation and to demonstrate compliance with NF EN ISO 23418 for quality-aware WGS analysis.
Felten et al. (Mon,) studied this question.