Department of Medical Microbiology and Radboudumc Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Medical Microbiology, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, The Netherlands.
Microb Genom. 2021 Aug;7(8). doi: 10.1099/mgen.0.000612.
Whole-genome sequencing is becoming the standard for bacterial outbreak surveillance and infection prevention. This is accompanied by a variety of bioinformatic tools and needs bioinformatics expertise for implementation. However, little is known about the concordance of reported outbreaks when using different bioinformatic workflows. In this multi-centre proficiency testing among 13 major Dutch healthcare-affiliated centres, bacterial whole-genome outbreak analysis was assessed. Centres who participated obtained two randomized bacterial datasets of Illumina sequences, a and a Vancomycin-resistant and were asked to apply their bioinformatic workflows. Centres reported back on antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters. The reported clusters were analysed using a method to compare landscapes of phylogenetic trees and calculating Kendall-Colijn distances. Furthermore, fasta files were analysed by state-of-the-art single nucleotide polymorphism (SNP) analysis to mitigate the differences introduced by each centre and determine standardized SNP cut-offs. Thirteen centres participated in this study. The reported outbreak clusters revealed discrepancies between centres, even when almost identical bioinformatic workflows were used. Due to stringent filtering, some centres failed to detect extended-spectrum beta-lactamase genes and MLST loci. Applying a standardized method to determine outbreak clusters on the reported assemblies, did not result in uniformity of outbreak-cluster composition among centres.
全基因组测序正成为细菌暴发监测和感染预防的标准。这伴随着各种生物信息学工具，并需要生物信息学专业知识来实施。然而，当使用不同的生物信息学工作流程时，报告的暴发之间的一致性知之甚少。在这项针对 13 个主要荷兰医疗机构的多中心能力验证中，对细菌全基因组暴发分析进行了评估。参与的中心获得了两个随机的 Illumina 序列细菌数据集， 和一个 ，并被要求应用他们的生物信息学工作流程。中心报告了抗生素耐药性、多位点序列分型（MLST）和暴发聚类。使用一种比较系统发育树景观和计算 Kendall-Colijn 距离的方法分析报告的聚类。此外，通过最先进的单核苷酸多态性（SNP）分析分析 fasta 文件，以减轻每个中心引入的差异，并确定标准化的 SNP 截止值。13 个中心参与了这项研究。报告的暴发集群显示出中心之间的差异，即使使用几乎相同的生物信息学工作流程也是如此。由于严格的过滤，一些中心未能检测到扩展谱β-内酰胺酶基因和 MLST 基因座。应用标准化方法确定报告的 组装体上的暴发集群，并没有导致中心之间的暴发集群组成的一致性。
