Public health authorities whole-genome sequence thousands of isolates each month for microbial diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the deluge of data and the need for real-time results. We have therefore created a bioinformatics pipeline for rapid subtyping and continuous phylogenomic analysis of bacterial samples, suited for large-scale surveillance. The data is divided into sets by mapping to reference genomes, then consensus sequences are generated. Nucleotide based genetic distance is calculated between the sequences in each set, and isolates are clustered together at 10 single-nucleotide polymorphisms. Phylogenetic trees are inferred from the non-redundant sequences and the clustered isolates are added back. The method is accurate at grouping outbreak strains together, while discriminating them from non-outbreak strains. The pipeline is applied in Evergreen Online, which processes publicly available sequencing data from foodborne bacterial pathogens on a daily basis, updating phylogenetic trees as needed.

公共卫生部门每月对全基因组序列进行数千个分离，以进行微生物诊断和病原菌监测。计算方法未能跟上大量数据和对实时结果的需求。因此，我们创建了一条生物信息学流水线，用于细菌样品的快速亚型分析和连续系统生物学分析，适用于大规模监测。通过映射到参考基因组将数据分为几组，然后生成共有序列。计算每组序列之间基于核苷酸的遗传距离，并将分离物以10个单核苷酸多态性聚类在一起。从非冗余序列推断出系统发育树，然后将聚簇的分离物加回去。该方法可以准确地将爆发菌株分组在一起，同时将它们与非暴发株区分开来。该管道用于Evergreen Online，该系统每天处理食源性细菌病原体的公开测序数据，并根据需要更新系统发育树。