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1.
BMC Infect Dis ; 22(1): 558, 2022 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-35718768

RESUMO

BACKGROUND: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. METHOD: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. RESULT: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities. CONCLUSION: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.


Assuntos
COVID-19 , Infecções por Coronavirus , Pneumonia Viral , Betacoronavirus , COVID-19/tratamento farmacológico , COVID-19/epidemiologia , Estudo de Associação Genômica Ampla , Humanos , SARS-CoV-2/genética , Vietnã/epidemiologia
2.
Arch Virol ; 165(12): 2921-2926, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32989573

RESUMO

In this study, we present an analysis of metagenome sequences obtained from a filtrate of a siphon tissue homogenate of otter clams (Lutraria rhynchaena) with swollen-siphon disease. The viral signal was mined from the metagenomic data, and a novel circular ssDNA virus was identified. Genomic features and phylogenetic analysis showed that the virus belongs to the phylum Cressdnaviricota, which consists of viruses with circular, single-stranded DNA (ssDNA) genomes. Members of this phylum have been identified in various species and in environmental samples. The newly found virus is distantly related to the currently known members of the phylum Cressdnaviricota.


Assuntos
Bivalves/genética , Vírus de DNA/classificação , DNA Viral/genética , Genoma Viral , Animais , Vírus de DNA/isolamento & purificação , DNA Circular/genética , DNA de Cadeia Simples/genética , Microbiologia Ambiental , Metagenômica , Filogenia , Análise de Sequência de DNA
3.
BMC Bioinformatics ; 21(1): 244, 2020 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-32539680

RESUMO

BACKGROUND: The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. RESULTS: To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments. CONCLUSIONS: Considering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments.


Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes/genética , MicroRNAs/genética , Humanos
4.
Microbiol Resour Announc ; 9(2)2020 Jan 09.
Artigo em Inglês | MEDLINE | ID: mdl-31919158

RESUMO

Otter clam farming in Vietnam has recently encountered difficulties due to swollen-siphon disease. Here, we report the metagenome sequences of microorganisms extracted from the siphon tissue of infected otter clams. The data comprised bacterial and viral sequences which likely include those derived from the disease-causing agent.

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