A Summary of Virus Classification

2021 ◽  
pp. 3-5
Author(s):  
Hans-W. Ackermann ◽  
Laurent Berthiaume ◽  
Michel Tremblay
Keyword(s):  
Virus Classification

COMPARING VIRUS CLASSIFICATION USING GENOMIC MATERIALS ACCORDING TO DIFFERENT TAXONOMIC LEVELS

2013 ◽  
Vol 11 (06) ◽  
pp. 1343003 ◽  
Author(s):  
JING-DOO WANG
Keyword(s):  
Molecular Biology ◽  
Dna Sequences ◽  
Protein Sequences ◽  
Experimental Results ◽  
Virus Classification ◽  
Taxonomic Level ◽  
Biological Classification ◽  
Novel Approach

In this paper, three genomic materials — DNA sequences, protein sequences, and regions (domains) are used to compare methods of virus classification. Virus classes (categories) are divided by various taxonomic level of virus into three datasets for 6 order, 42 family, and 33 genera. To increase the robustness and comparability of experimental results of virus classification, the classes are selected that contain at least 10 instances, and meanwhile each instance contains at least one region name. Experimental results show that the approach using region names achieved the best accuracies — reaching 99.9%, 97.3%, and 99.0% for 6 orders, 42 families, and 33 genera, respectively. This paper not only involves exhaustive experiments that compare virus classifications using different genomic materials, but also proposes a novel approach to biological classification based on molecular biology instead of traditional morphology.

Human influenza: a virus classification using a probabilistic neural network

Kybernetes ◽  
2008 ◽  
Vol 37 (9/10) ◽  
pp. 1425-1430 ◽  
Author(s):  
Zheng Kou ◽  
Yanhong Zhou ◽  
Xiaoli Qiang
Keyword(s):  
Neural Network ◽  
Influenza A Virus ◽  
Influenza A ◽  
Probabilistic Neural Network ◽  
Human Influenza ◽  
Virus Classification

scholarly journals Wild-Type Gross Leukemia Virus: Classification of Soluble Antigens (GSA)

1972 ◽  
Vol 10 (6) ◽  
pp. 1208-1219 ◽  
Author(s):  
Tadao Aoki ◽  
Ronald B. Herberman ◽  
Patricia A. Johnson ◽  
Margaret Liu ◽  
Macie M. Sturm
Keyword(s):  
Leukemia Virus ◽  
Wild Type ◽  
Virus Classification

scholarly journals Automated, phylogeny-based genotype delimitation of the Hepatitis Viruses HBV and HCV

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7754
Author(s):  
Dora Serdari ◽  
Evangelia-Georgia Kostaki ◽  
Dimitrios Paraskevis ◽  
Alexandros Stamatakis ◽  
Paschalia Kapli
Keyword(s):  
Species Delimitation ◽  
Ad Hoc ◽  
Hepatitis Virus ◽  
Genetic Distances ◽  
Phenotypic Traits ◽  
Virus Taxonomy ◽  
Hepatitis Viruses ◽  
Virus Classification ◽  
Distance Methods

Background The classification of hepatitis viruses still predominantly relies on ad hoc criteria, i.e., phenotypic traits and arbitrary genetic distance thresholds. Given the subjectivity of such practices coupled with the constant sequencing of samples and discovery of new strains, this manual approach to virus classification becomes cumbersome and impossible to generalize. Methods Using two well-studied hepatitis virus datasets, HBV and HCV, we assess if computational methods for molecular species delimitation that are typically applied to barcoding biodiversity studies can also be successfully deployed for hepatitis virus classification. For comparison, we also used ABGD, a tool that in contrast to other distance methods attempts to automatically identify the barcoding gap using pairwise genetic distances for a set of aligned input sequences. Results—Discussion We found that the mPTP species delimitation tool identified even without adapting its default parameters taxonomic clusters that either correspond to the currently acknowledged genotypes or to known subdivision of genotypes (subtypes or subgenotypes). In the cases where the delimited cluster corresponded to subtype or subgenotype, there were previous concerns that their status may be underestimated. The clusters obtained from the ABGD analysis differed depending on the parameters used. However, under certain values the results were very similar to the taxonomy and mPTP which indicates the usefulness of distance based methods in virus taxonomy under appropriate parameter settings. The overlap of predicted clusters with taxonomically acknowledged genotypes implies that virus classification can be successfully automated.

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