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Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis

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 seq...

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Autores principales: Szabó, Péter Márton, Szalay, Dóra, Kecskeméti, Sándor, Molnár, Tamás, Szabó, István, Bálint, Ádám
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645787/
https://www.ncbi.nlm.nih.gov/pubmed/33154401
http://dx.doi.org/10.1038/s41598-020-75516-5
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author Szabó, Péter Márton
Szalay, Dóra
Kecskeméti, Sándor
Molnár, Tamás
Szabó, István
Bálint, Ádám
author_facet Szabó, Péter Márton
Szalay, Dóra
Kecskeméti, Sándor
Molnár, Tamás
Szabó, István
Bálint, Ádám
author_sort Szabó, Péter Márton
collection PubMed
description 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.
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spelling pubmed-76457872020-11-06 Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis Szabó, Péter Márton Szalay, Dóra Kecskeméti, Sándor Molnár, Tamás Szabó, István Bálint, Ádám Sci Rep Article 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. Nature Publishing Group UK 2020-11-05 /pmc/articles/PMC7645787/ /pubmed/33154401 http://dx.doi.org/10.1038/s41598-020-75516-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Szabó, Péter Márton
Szalay, Dóra
Kecskeméti, Sándor
Molnár, Tamás
Szabó, István
Bálint, Ádám
Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title_full Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title_fullStr Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title_full_unstemmed Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title_short Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis
title_sort investigations on spreading of prrsv among swine herds by improved minimum spanning network analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645787/
https://www.ncbi.nlm.nih.gov/pubmed/33154401
http://dx.doi.org/10.1038/s41598-020-75516-5
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