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1.
Microbiol Resour Announc ; 13(2): e0089123, 2024 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-38189309

RESUMO

Historically, genotyping of African swine fever virus was based on partial sequencing of B646L (p72). Until recently, the number of differences that defined genotypes was ambiguous. This tool allows a sequence to be uploaded and will report its closest matches along with its likely p72 genotype.

2.
Viruses ; 15(11)2023 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-38005923

RESUMO

The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.


Assuntos
Vírus da Febre Suína Africana , Febre Suína Africana , Animais , Febre Suína Africana/epidemiologia , Febre Suína Africana/virologia , Vírus da Febre Suína Africana/genética , Genótipo , Filogenia , Análise de Sequência , Sus scrofa , Suínos
3.
Viruses ; 16(1)2023 12 30.
Artigo em Inglês | MEDLINE | ID: mdl-38257767

RESUMO

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150-200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.


Assuntos
Vírus da Febre Suína Africana , Febre Suína Africana , Animais , Suínos , Vírus da Febre Suína Africana/genética , Febre Suína Africana/epidemiologia , Aprendizado de Máquina não Supervisionado , Genótipo , Algoritmos
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