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Real Time Classification of Viruses in 12 Dimensions
The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing label rates in GenBank are 12.1% for family label a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3661469/ https://www.ncbi.nlm.nih.gov/pubmed/23717598 http://dx.doi.org/10.1371/journal.pone.0064328 |
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author | Yu, Chenglong Hernandez, Troy Zheng, Hui Yau, Shek-Chung Huang, Hsin-Hsiung He, Rong Lucy Yang, Jie Yau, Stephen S.-T. |
author_facet | Yu, Chenglong Hernandez, Troy Zheng, Hui Yau, Shek-Chung Huang, Hsin-Hsiung He, Rong Lucy Yang, Jie Yau, Stephen S.-T. |
author_sort | Yu, Chenglong |
collection | PubMed |
description | The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing label rates in GenBank are 12.1% for family label and 30.0% for genus label. Using the proposed Natural Vector representation, all 2,044 single-segment referenced viral genomes in GenBank can be embedded in [Image: see text]. Unlike other approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family, subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides a visualization of the distribution of viruses in [Image: see text]. Unlike the commonly used tree visualization methods which suffer from uniqueness and existence problems, our representation always exists and is unique. This approach is successfully used to predict and correct viral classification information, as well as to identify viral origins; e.g. a recent public health threat, the West Nile virus, is closer to the Japanese encephalitis antigenic complex based on our visualization. Based on cross-validation results, the accuracy rates of our predictions are as high as 98.2% for Baltimore class labels, 96.6% for family labels, 99.7% for subfamily labels and 97.2% for genus labels. |
format | Online Article Text |
id | pubmed-3661469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36614692013-05-28 Real Time Classification of Viruses in 12 Dimensions Yu, Chenglong Hernandez, Troy Zheng, Hui Yau, Shek-Chung Huang, Hsin-Hsiung He, Rong Lucy Yang, Jie Yau, Stephen S.-T. PLoS One Research Article The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing label rates in GenBank are 12.1% for family label and 30.0% for genus label. Using the proposed Natural Vector representation, all 2,044 single-segment referenced viral genomes in GenBank can be embedded in [Image: see text]. Unlike other approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family, subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides a visualization of the distribution of viruses in [Image: see text]. Unlike the commonly used tree visualization methods which suffer from uniqueness and existence problems, our representation always exists and is unique. This approach is successfully used to predict and correct viral classification information, as well as to identify viral origins; e.g. a recent public health threat, the West Nile virus, is closer to the Japanese encephalitis antigenic complex based on our visualization. Based on cross-validation results, the accuracy rates of our predictions are as high as 98.2% for Baltimore class labels, 96.6% for family labels, 99.7% for subfamily labels and 97.2% for genus labels. Public Library of Science 2013-05-22 /pmc/articles/PMC3661469/ /pubmed/23717598 http://dx.doi.org/10.1371/journal.pone.0064328 Text en © 2013 Yu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Yu, Chenglong Hernandez, Troy Zheng, Hui Yau, Shek-Chung Huang, Hsin-Hsiung He, Rong Lucy Yang, Jie Yau, Stephen S.-T. Real Time Classification of Viruses in 12 Dimensions |
title | Real Time Classification of Viruses in 12 Dimensions |
title_full | Real Time Classification of Viruses in 12 Dimensions |
title_fullStr | Real Time Classification of Viruses in 12 Dimensions |
title_full_unstemmed | Real Time Classification of Viruses in 12 Dimensions |
title_short | Real Time Classification of Viruses in 12 Dimensions |
title_sort | real time classification of viruses in 12 dimensions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3661469/ https://www.ncbi.nlm.nih.gov/pubmed/23717598 http://dx.doi.org/10.1371/journal.pone.0064328 |
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