Where classical epidemiology has proven to be inadequate for surveillance and control of foodborne pathogens, molecular epidemiology, using genomic typing methods, can add value. However, the analysis of whole genome sequencing (WGS) data varies widely and is not yet fully harmonised. We used genomic data on 494 Listeria monocytogenes isolates from ready-to-eat food products and food processing environments deposited in the strain collection of the German National Reference Laboratory to compare various procedures for WGS data analysis and to evaluate compatibility of results. Two different core genome multilocus sequence typing (cgMLST) schemes, different reference genomes in single nucleotide polymorphism (SNP) analysis and commercial as well as open-source software were compared. Correlation of allele distances from the different cgMLST approaches was high, ranging from 0.97 to 1, and unified thresholds yielded higher clustering concordance than scheme-specific thresholds. The number of detected SNP differences could be increased up to a factor of 3.9 using a specific reference genome compared with a general one. Additionally, specific reference genomes improved comparability of SNP analysis results obtained using different software tools. The use of a closed or a draft specific reference genome did not make a difference. The harmonisation of WGS data analysis will finally guarantee seamless data exchange, but, in the meantime, knowledge on threshold values that lead to comparable clustering of isolates by different methods may improve communication between laboratories. We therefore established a translation code between commonly applied cgMLST and SNP methods based on optimised clustering concordances. This code can work as a first filter to identify WGS-based typing matches resulting from different methods, which opens up a new perspective for data exchange and thereby accelerates time-critical analyses, such as in outbreak investigations.

中文翻译：

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WGS 分型结果的可翻译性可以简化单核细胞增生李斯特菌监测和控制的数据交换


在传统流行病学已被证明不足以监测和控制食源性病原体的情况下，使用基因组分型方法的分子流行病学可以增加价值。然而，全基因组测序（WGS）数据的分析差异很大，而且尚未完全统一。我们使用来自德国国家参考实验室菌株保藏库的即食食品和食品加工环境中的 494 株单增李斯特菌分离株的基因组数据来比较各种 WGS 数据分析程序并评估结果的兼容性。比较了两种不同的核心基因组多位点序列分型（cgMLST）方案、单核苷酸多态性（SNP）分析中的不同参考基因组以及商业和开源软件。不同 cgMLST 方法的等位基因距离相关性很高，范围为 0.97 到 1，并且统一阈值比特定方案阈值产生更高的聚类一致性。与一般参考基因组相比，使用特定参考基因组检测到的 SNP 差异数量最多可增加 3.9 倍。此外，特定的参考基因组提高了使用不同软件工具获得的 SNP 分析结果的可比性。使用封闭的或草稿的特定参考基因组并没有产生什么影响。 WGS 数据分析的统一将最终保证无缝数据交换，但与此同时，通过不同方法对分离株进行可比聚类的阈值知识可能会改善实验室之间的沟通。因此，我们基于优化的聚类一致性在常用的 cgMLST 和 SNP 方法之间建立了翻译代码。 该代码可以作为第一个过滤器来识别由不同方法产生的基于 WGS 的打字匹配，这为数据交换开辟了新的视角，从而加速了时间关键的分析，例如在疫情调查中。