ArticlePLoS computational biology2012
Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 32 citations in OpenAlex.
- In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest.Nature communications · 2025Article
- Neutralization potency of the 2023-24 seasonal influenza vaccine against circulating influenza H3N2 strains.Human vaccines & immunotherapeutics · 2024Article
- Seasonal antigenic prediction of influenza A H3N2 using machine learning.Nature communications · 2024Article
- Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).GigaScience · 2024Article
- Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition.Frontiers in bioinformatics · 2023Article
- Optimal sequence-based design for multi-antigen HIV-1 vaccines using minimally distant antigens.PLoS computational biology · 2022Article
- Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022Review
- Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses.Molecular biology and evolution · 2021Article
- Allele-specific nonstationarity in evolution of influenza A virus surface proteins.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Structures and functions linked to genome-wide adaptation of human influenza A viruses.Scientific reports · 2019Article
- Bioinformatics Meets Virology: The European Virus Bioinformatics Center's Second Annual Meeting.Viruses · 2018Article
- Models for predicting the evolution of influenza to inform vaccine strain selection.Human vaccines & immunotherapeutics · 2018Review
- Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.Trends in microbiology · 2018Review
- Identification of Low- and High-Impact Hemagglutinin Amino Acid Substitutions That Drive Antigenic Drift of Influenza A(H1N1) Viruses.PLoS pathogens · 2016Article
- Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses.Virus evolution · 2016Article
- Coordinated Evolution of Influenza A Surface Proteins.PLoS genetics · 2015Article
- Computational prediction of vaccine strains for human influenza A (H3N2) viruses.Journal of virology · 2014Article
- RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees.Bioinformatics (Oxford, England) · 2014Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Distinguishing mutations that determine an organism's phenotype from (near-) neutral 'hitchhikers' is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring 'antigenic trees' for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of 'phenotype trees' and genotype-phenotype relationships from other types of pairwise phenotype distances.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.