摘要：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.
其他摘要：Abstract 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.
