Whole-genome sequencing (WGS) is becoming the de facto standard for bacterial typing and outbreak surveillance of resistant bacterial pathogens. However, interoperability for WGS of bacterial outbreaks is poorly understood. We hypothesized that harmonization of WGS for outbreak surveillance is achievable through the use of identical protocols for both data generation and data analysis. A set of 30 bacterial isolates, comprising of various species belonging to the Enterobacteriaceae family and Enterococcus genera, were selected and sequenced using the same protocol on the Illumina MiSeq platform in each individual centre. All generated sequencing data were analysed by one centre using BioNumerics (6.7.3) for (i) genotyping origin of replications and antimicrobial resistance genes, (ii) core-genome multi-locus sequence typing (cgMLST) for Escherichia coli and Klebsiella pneumoniae and whole-genome multi-locus sequencing typing (wgMLST) for all species. Additionally, a split k-mer analysis was performed to determine the number of SNPs between samples. A precision of 99.0% and an accuracy of 99.2% was achieved for genotyping. Based on cgMLST, a discrepant allele was called only in 2/27 and 3/15 comparisons between two genomes, for E. coli and K. pneumoniae, respectively. Based on wgMLST, the number of discrepant alleles ranged from 0 to 7 (average 1.6). For SNPs, this ranged from 0 to 11 SNPs (average 3.4). Furthermore, we demonstrate that using different de novo assemblers to analyse the same dataset introduces up to 150 SNPs, which surpasses most thresholds for bacterial outbreaks. This shows the importance of harmonization of data-processing surveillance of bacterial outbreaks. In summary, multi-centre WGS for bacterial surveillance is achievable, but only if protocols are harmonized.

中文翻译：

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协调用于肠杆菌科和肠球菌暴发监测的全基因组测序

全基因组测序 (WGS) 正在成为细菌分型和耐药细菌病原体爆发监测的事实上的标准。然而，人们对细菌暴发的 WGS 的互操作性知之甚少。我们假设通过使用相同的数据生成和数据分析协议可以实现 WGS 暴发监测的协调。一组 30 种细菌分离物，包括属于肠杆菌科和肠球菌的各种物种 在每个独立中心的 Illumina MiSeq 平台上使用相同的协议选择和测序属。一个中心使用 BioNumerics (6.7.3) 分析所有生成的测序数据，用于 (i) 对复制起点和抗菌素耐药基因进行基因分型，(ii)大肠杆菌和肺炎克雷伯菌的核心基因组多位点序列分型 (cgMLST)和所有物种的全基因组多位点测序分型 (wgMLST)。此外，进行分裂k -mer 分析以确定样品之间的 SNP 数量。基因分型的精确度为 99.0%，准确度为 99.2%。基于 cgMLST，对于大肠杆菌，仅在两个基因组之间的 2/27 和 3/15 比较中调用了差异等位基因 和肺炎克雷伯菌，分别。根据 wgMLST，差异等位基因的数量范围为 0 到 7（平均 1.6）。对于 SNP，范围为 0 到 11 个 SNP（平均 3.4）。此外，我们证明使用不同的从头组装器来分析相同的数据集会引入多达 150 个 SNP，这超过了细菌爆发的大多数阈值。这表明了对细菌爆发的数据处理监测进行协调的重要性。总之，用于细菌监测的多中心 WGS 是可以实现的，但前提是协议必须协调一致。