Hungarian Academy of Sciences and Semmelweis University, Szigony u. 43., Budapest, 1083, Hungary.
Department of Virology, National Food Chain Safety Office Veterinary Diagnostic Directorate, Tabornok u. 2., Budapest, 1143, Hungary.
Sci Rep. 2020 Nov 5;10(1):19217. doi: 10.1038/s41598-020-75516-5.
In Hungary, the economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV) led to the launching of a national PRRSV Eradication Program. An important element of the program was investigating the spread of PRRSV among swine herds and the possible ways of introduction by sequencing of the open reading frame 5 (ORF5) gene. However, the classical phylogenetic tree presentation cannot explain several genetic relationships clearly, while more precise visualization can be represented by network tree diagram. In this paper, we describe a practical and easy-to-follow enriched minimum spanning similarity network application for improved representation of phylogenetic relations among viral strains. This method eliminated the necessity of applying a predefined, arbitrary cut-off or computationally extensive algorithms. The network-based visualization allowed processing and visualizing large amount of data equally for the laboratory, private and official veterinarians, and helped identify the potential connections between different viral sequences that support data-driven decisions in the eradication program. By applying network analysis, previously unknown epidemiological connections between infected herds were identified, and virus spreading was analyzed within short period of time. In our study, we successfully built and applied network analysis tools in the course of the Hungarian PRRSV Eradication Program.
在匈牙利，猪繁殖与呼吸综合征病毒（PRRSV）造成的经济损失导致了国家 PRRSV 根除计划的启动。该计划的一个重要组成部分是通过测序开放阅读框 5（ORF5）基因来调查猪群中 PRRSV 的传播及其可能的传入途径。然而，经典的系统发生树表示法不能清楚地解释几种遗传关系，而更精确的可视化可以通过网络图表示。在本文中，我们描述了一种实用且易于遵循的富集最小生成树相似性网络应用程序，用于改进病毒株之间系统发生关系的表示。该方法消除了应用预定义的、任意的截止值或计算密集型算法的必要性。基于网络的可视化允许对实验室、私人和官方兽医进行大量数据的处理和可视化，有助于识别不同病毒序列之间的潜在联系，从而支持根除计划中的数据驱动决策。通过应用网络分析，确定了受感染畜群之间以前未知的流行病学联系，并在短时间内分析了病毒的传播情况。在我们的研究中，我们成功地在匈牙利 PRRSV 根除计划中构建和应用了网络分析工具。
