McHugh Martin P, Horsfield Samuel T, von Wachsmann Johanna, Toussaint Jacqueline, Pettigrew Kerry A, Czarniak Elzbieta, Evans Thomas J, Leanord Alistair, Tysall Luke, Gillespie Stephen H, Templeton Kate E, Holden Matthew T G, Croucher Nicholas J, Lees John A
Medical Microbiology, Department of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK.
Division of Infection and Global Health, University of St Andrews, St Andrews KY16 9AJ, UK.
Microb Genom. 2025 Apr;11(4). doi: 10.1099/mgen.0.001404.
Genetic distances between bacterial DNA sequences can be used to cluster populations into closely related subpopulations and as an additional source of information when detecting possible transmission events. Due to their variable gene content and order, reference-free methods offer more sensitive detection of genetic differences, especially among closely related samples found in outbreaks. However, across longer genetic distances, frequent recombination can make calculation and interpretation of these differences more challenging, requiring significant bioinformatic expertise and manual intervention during the analysis process. Here, we present a ulation analysis line (PopPIPE) which combines rapid reference-free genome analysis methods to analyse bacterial genomes across these two scales, splitting whole populations into subclusters and detecting plausible transmission events within closely related clusters. We use k-mer sketching to split populations into strains, followed by split k-mer analysis and recombination removal to create alignments and subclusters within these strains. We first show that this approach creates high-quality subclusters on a population-wide dataset of . When applied to nosocomial vancomycin-resistant samples, PopPIPE finds transmission clusters that are more epidemiologically plausible than core genome or multilocus sequence typing (MLST) approaches. Our pipeline is rapid and reproducible, creates interactive visualizations and can easily be reconfigured and re-run on new datasets. Therefore, PopPIPE provides a user-friendly pipeline for analyses spanning species-wide clustering to outbreak investigations.
细菌DNA序列之间的遗传距离可用于将群体聚类为密切相关的亚群体，并在检测可能的传播事件时作为额外的信息来源。由于其可变的基因含量和顺序，无参考方法能更灵敏地检测遗传差异，尤其是在疫情中发现的密切相关样本之间。然而，在更长的遗传距离上，频繁的重组会使这些差异的计算和解释更具挑战性，在分析过程中需要大量的生物信息学专业知识和人工干预。在此，我们提出了一种群体分析流程（PopPIPE），它结合了快速的无参考基因组分析方法，以跨这两个尺度分析细菌基因组，将整个群体划分为亚群，并在密切相关的集群中检测可能的传播事件。我们使用k-mer草图将群体划分为菌株，然后进行分裂k-mer分析和重组去除，以在这些菌株中创建比对和亚群。我们首先表明，这种方法在一个包含[具体数量]的全群体数据集上创建了高质量的亚群。当应用于医院获得性耐万古霉素[具体细菌名称]样本时，PopPIPE发现的传播集群在流行病学上比核心基因组或多位点序列分型（MLST）方法更合理。我们的流程快速且可重复，能创建交互式可视化效果，并且可以轻松地在新数据集上重新配置和重新运行。因此，PopPIPE为从物种范围的聚类到疫情调查的分析提供了一个用户友好的流程。
