Pavlovikj Natasha, Gomes-Neto Joao Carlos, Deogun Jitender S, Benson Andrew K
School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Life (Basel). 2022 Apr 30;12(5):670. doi: 10.3390/life12050670.
Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turnaround, while aiming at generating two main outcomes: (1) species-level identification and (2) variant mapping at different levels of genotypic resolution for population-based tracking and surveillance, in addition to predicting traits such as antimicrobial resistance (AMR). Multi-locus sequence typing (MLST) aids this process by identifying sequence types (ST) based on seven ubiquitous genome-scattered loci. In this paper, we selected one assembly-dependent and one assembly-free method for ST mapping and applied them with the default settings and ST schemes they are distributed with, and systematically assessed their accuracy and scalability across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens with available MLST databases. Our data show that the optimal k-mer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect the performance and accuracy of the program. Although suitable parameters could be identified for most organisms, there were instances where this program may not be directly deployable in its current format. Next, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis.
对细菌病原体的流行病学监测需要进行快速周转的实时数据分析，同时旨在产生两个主要结果：（1）物种水平的鉴定，以及（2）在不同基因型分辨率水平上进行变异体图谱分析，以用于基于群体的追踪和监测，此外还要预测诸如抗菌药物耐药性（AMR）等特征。多位点序列分型（MLST）通过基于七个普遍存在的分散于基因组中的位点来识别序列类型（ST），从而辅助这一过程。在本文中，我们选择了一种依赖组装和一种不依赖组装的方法进行ST图谱分析，并以它们所附带的默认设置和ST方案来应用它们，然后系统地评估了它们在一系列系统发育上不同的、与公共卫生相关的、拥有可用MLST数据库的细菌病原体中的准确性和可扩展性。我们的数据表明，stringMLST的最佳k-mer长度是物种特异性的，并且基因组内在和外在特征会影响该程序的性能和准确性。虽然可以为大多数生物体确定合适的参数，但在某些情况下，该程序目前的格式可能无法直接部署。接下来，我们将stringMLST集成到我们免费可用且可扩展的基于分层的群体基因组学平台ProkEvo中，并进一步展示了这种整合如何促进自动化、可重复的细菌群体分析。
