ArticleVirus evolution2019
Influenza passaging annotations: what they tell us and why we should listen.
Article in Virus evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Seasonal antigenic prediction of influenza A H3N2 using machine learning.Nature communications · 2024Article
- Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus.Viruses · 2022Article
- Human Organs-on-Chips for Virology.Trends in microbiology · 2020Review
- Drivers of recombinant soluble influenza A virus hemagglutinin and neuraminidase expression in mammalian cells.Protein science : a publication of the Protein Society · 2020Article
- Article
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Authors and funding
6 authors.
Funding
Abstract
Influenza databases now contain over 100,000 worldwide sequence records for strains influenza A(H3N2) and A(H1N1). Although these data facilitate global research efforts and vaccine development practices, they also represent a stumbling block for researchers because of their confusing and heterogeneous annotation. Unclear passaging annotations are particularly concerning given the recent work highlighting the presence and risk of false adaptation signals introduced by cell passaging of viral isolates. With this in mind, we aim to provide a concise outline of why viruses are passaged, a clear overview of passaging annotation nomenclature currently in use, and suggestions for a standardized nomenclature going forward. Our hope is that this summary will empower researchers and clinicians alike to more easily understand a virus sample's passage history when analyzing influenza sequences.
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Registered trials
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