Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene
Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fr...
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| Format: | Article |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2021.683134/full |
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| author | Jeonghoon Kim Kyuyoung Lee Ruwini Rupasinghe Shahbaz Rezaei Beatriz Martínez-López Xin Liu |
| author_facet | Jeonghoon Kim Kyuyoung Lee Ruwini Rupasinghe Shahbaz Rezaei Beatriz Martínez-López Xin Liu |
| author_sort | Jeonghoon Kim |
| collection | DOAJ |
| description | Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance. |
| first_indexed | 2024-12-16T23:11:26Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2297-1769 |
| language | English |
| last_indexed | 2024-12-16T23:11:26Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Veterinary Science |
| spelling | doaj.art-d5960785c3d94045aa24fbc3d6cb84b92022-12-21T22:12:24ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692021-07-01810.3389/fvets.2021.683134683134Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 GeneJeonghoon Kim0Kyuyoung Lee1Ruwini Rupasinghe2Shahbaz Rezaei3Beatriz Martínez-López4Xin Liu5Department of Mathematics, University of California, Davis, Davis, CA, United StatesDepartment of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United StatesDepartment of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United StatesDepartment of Computer Science, University of California, Davis, Davis, CA, United StatesDepartment of Medicine and Epidemiology, Center for Animal Disease Modeling and Surveillance (CADMS), School of Veterinary Medicine, University of California, Davis, Davis, CA, United StatesDepartment of Computer Science, University of California, Davis, Davis, CA, United StatesPorcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.https://www.frontiersin.org/articles/10.3389/fvets.2021.683134/fullartificial intelligencerandom forestk-nearest neighborsupport vector machineswine healthphylogenetic tree |
| spellingShingle | Jeonghoon Kim Kyuyoung Lee Ruwini Rupasinghe Shahbaz Rezaei Beatriz Martínez-López Xin Liu Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene Frontiers in Veterinary Science artificial intelligence random forest k-nearest neighbor support vector machine swine health phylogenetic tree |
| title | Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene |
| title_full | Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene |
| title_fullStr | Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene |
| title_full_unstemmed | Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene |
| title_short | Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene |
| title_sort | applications of machine learning for the classification of porcine reproductive and respiratory syndrome virus sublineages using amino acid scores of orf5 gene |
| topic | artificial intelligence random forest k-nearest neighbor support vector machine swine health phylogenetic tree |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2021.683134/full |
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