Amplicon sequencing of diverse viruses with 3’ anchored primer clouds
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB81871 | ENA |
REPOSITORIES: ENA
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Project description:Human parainfluenza viruses (hPIVs) are major contributors to respiratory tract infections in young children worldwide. Despite their global significance, genomic surveillance of hPIV1 and hPIV2 had not previously been conducted in Russia. This study aimed to develop a robust amplicon-based sequencing protocol for these viruses. The designed primer sets were tested on clinical samples containing hPIV RNA to evaluate their performance and efficiency. Sequencing results demonstrated high-quality genome data and efficient amplification across various Ct values. As a result, 41 hPIV1 and 13 hPIV2 near-complete genome sequences were successfully obtained from clinical specimens collected in Saint Petersburg (Russia). Phylogenetic analysis of the HN gene sequences showed that Russian hPIV1 strains clustered into clades II and III, while hPIV2 strains were distributed between clusters G1a and G3. The whole-genome-based trees confirmed the same distribution of the strains. These findings highlight the potential of our primer panels and contribute to a better understanding of the molecular characteristics and phylogenetic diversity of circulating hPIV strains. Notably, this study presents the first evolutionary analysis of hPIVs in Russia.
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2021-05-19 | PXD021799 | Pride
Project description:Microbiome PCR primer model is a Named Entity Recognition (NER) model that identifies and annotates microbiome target gene primers in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with primer metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
2022-02-21 | MODEL2202170011 | BioModels