一个开源的仅纳米孔测序工作流程用于分析克隆爆发,提供短读水平准确度
Nick Vereecke1, Thomas B Yoon1, Ting L Luo2
1Bacterial Pathogenesis and Antimicrobial Resistance Section (BPARS), Laboratory of Clinical Immunology & Microbiology (LCIM), National Institute for Allergy and Infectious Disease (NIAID), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Journal of clinical microbiology
|July 18, 2025
概括
这项研究优化了用于细菌爆发分析的仅纳米孔测序工作流,取得了与传统短读方法相似的结果. 这种开源方法通过快速准确的病原体识别来加强医院感染控制.
科学领域:
- 基因组学就是基因组学.
- 传染病流行病学 传染病流行病学
- 生物信息学是一种生物信息学.
背景情况:
- 短读测序一直是细菌全基因组测序和疫情追踪的标准.
- 像牛津纳米孔技术 (ONT) 这样的长期阅读测序平台在成本,便携性和速度方面具有优势.
- 从历史上看,ONT更高的基调错误率限制了其临床微生物学应用,包括疫情调查.
研究的目的:
- 为优化一个精简的,仅用于纳米孔的测序工作流,用于细菌病原体的流行病学分析.
- 为了验证本工作流的性能与已建立的短读序列化方法用于疫情追踪.
- 为全球医院疫情调查提供ONT-only基因组和核心基因组多部位序列类型 (cgMLST) 的更广泛实施.
主要方法:
- 优化了修改后的快速条码库准备策略,为高GC含量基因组提供温度.
- 对dorado套件 (v0.9.1) 的基调,错误纠正和抛光的性能进行了基准测试.
- 使用Flye进行长读组装和pyMLST进行cgMLST分析.
- 仅纳米孔的结果与Illumina短读测序数据进行了比较,以评估准确性.
主要成果:
- 使用dorado sup@v5.0.0基础调用与集成的错误校正和细菌模型抛光实现了最佳性能.
- 与短读引用相比,仅纳米孔组件显示出完全一致的基于cgMLST的最小跨越树.
- 全基因组分析显示了高一致性,与短读组件相比,每个基因组只有两个不一致的位置.
- 该工作流已经在各种临床疫情中成功验证,包括*Klebsiella pneumoniae*,*Pseudomonas aeruginosa*,*Enterococcus faecium*和*Staphylococcus aureus*.
结论:
- 优化的仅纳米孔工作流程为细菌克隆性和疫情分析提供了准确和可靠的结果.
- 这种开源工作流提供了与Illumina短读序列相美的性能,促进了快速的医院感染控制.
- 简化方法提高了用于常规临床微生物学和流行病学监测的仅使用ONT测序的可行性.
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