随着抗菌素耐药性(AMR)监测转向基因组学,确保跨实验室产生的全基因组测序(WGS)数据的质量至关重要.参与基因组能力测试(GPT)不仅增加了单个实验室的WGS能力,而且还提供了一个独特的机会,通过重复重新测序不同的分离株来提高WGS质量控制(QC)的物种特异性阈值。这里,我们介绍了2021年和2022年的欧盟抗菌素耐药性参考实验室(EURL-AR)网络GPT的结果,其中包括25个欧盟国家参考实验室(NLR).基于WGSQC度量评估了来自12个AMR细菌的总共392个基因组。百分之二(n=9)的数据被排除在外,由于污染,其余基因组中的11%(n=41)在至少一个QC度量中被鉴定为异常值,并被排除在调整后的QC阈值(AQT)的计算之外.通过线性回归确定了两个QC度量相关组。百分之八(n=28)提交的基因组,来自11个实验室,一个或多个AQT失败。然而,只有三个实验室(12%)被确定为表现不佳,至少两个基因组中不相关的QC指标在AQT中失败。最后,提出了“N50”和“重叠群数>200bp”的新物种特异性阈值,以指导常规实验室质量控制。NRL继续参与GPT将揭示WGS工作流程缺陷并改善AMR监测数据。GPT数据将继续为常规WGSQC制定可靠的特定物种阈值做出贡献。标准化测序数据QC,并确保国内和国内实验室的可比性。IMPORTANCEIllumina下一代测序是抗菌素耐药性(AMR)监测和最广泛使用的全基因组测序(WGS)平台的组成部分。高通量,相对低成本,具有很高的鉴别力,与经典生化方法相比,WGS的快速周转时间意味着该技术可能仍然是AMR监测和公共卫生的基本工具。在这项研究中,我们介绍了欧盟AMR网络参考实验室中国家参考实验室的WGS能力的当前水平,总结应用的方法,并对获得的序列数据的质量进行统计评估。这些发现为常规WGS中使用的质量指标设置新的和修订的阈值提供了基础,以前是任意定义的。此外,识别表现不佳的参与者,并鼓励他们评估他们的工作流程,以产生可靠的结果。
As antimicrobial resistance (AMR) surveillance shifts to genomics, ensuring the quality of whole-genome sequencing (WGS) data produced across laboratories is critical. Participation in genomic proficiency tests (GPTs) not only increases individual laboratories\' WGS capacity but also provides a unique opportunity to improve species-specific thresholds for WGS quality control (QC) by repeated resequencing of distinct isolates. Here, we present the results of the EU Reference Laboratory for Antimicrobial Resistance (EURL-AR) network GPTs of 2021 and 2022, which included 25 EU national reference laboratories (NLRs). A total of 392 genomes from 12 AMR-bacteria were evaluated based on WGS QC metrics. Two percent (n = 9) of the data were excluded, due to contamination, and 11% (n = 41) of the remaining genomes were identified as outliers in at least one QC metric and excluded from computation of the adjusted QC thresholds (AQT). Two QC metric correlation groups were identified through linear regression. Eight percent (n = 28) of the submitted genomes, from 11 laboratories, failed one or more of the AQTs. However, only three laboratories (12%) were identified as underperformers, failing across AQTs for uncorrelated QC metrics in at least two genomes. Finally, new species-specific thresholds for \"N50\" and \"number of contigs > 200 bp\" are presented for guidance in routine laboratory QC. The continued participation of NRLs in GPTs will reveal WGS workflow flaws and improve AMR surveillance data. GPT data will continue to contribute to the development of reliable species-specific thresholds for routine WGS QC, standardizing sequencing data QC and ensure inter- and intranational laboratory comparability.IMPORTANCEIllumina next-generation sequencing is an integral part of antimicrobial resistance (AMR) surveillance and the most widely used whole-genome sequencing (WGS) platform. The high-throughput, relative low-cost, high discriminatory power, and rapid turnaround time of WGS compared to classical biochemical methods means the technology will likely remain a fundamental tool in AMR surveillance and public health. In this study, we present the current level of WGS capacity among national reference laboratories in the EU Reference Laboratory for AMR network, summarizing applied methodology and statistically evaluating the quality of the obtained sequence data. These findings provide the basis for setting new and revised thresholds for quality metrics used in routine WGS, which have previously been arbitrarily defined. In addition, underperforming participants are identified and encouraged to evaluate their workflows to produce reliable results.