Full text
2,046 characters
· extracted from
oa-doi-fallback
· click to expand
Abstract
Influenza A viruses continuously undergo antigenic evolution to escape host immunity induced by previous infections or vaccinations, consequently causing seasonal epidemics and occasional pandemics. Antigenic prediction and visualization of influenza A viruses are crucial for precise vaccine strain selection and robust pandemic preparedness. However, a user-friendly online platform for these capabilities remains notably absent, despite widespread demand. Here, we present FluNexus (https://flunexus.com), the first-of-its-kind, one-stop-shop web platform designed to facilitate the prediction and visualization of the antigenic change in emerging variants. FluNexus features a data preprocessing module for hemagglutinin subunit 1 (HA1) and hemagglutination inhibition (HI) data across three major public health threat subtypes (H1, H3 and H5). Meanwhile, FluNexus provides an interactive interface for online antigenic prediction and offers practical guidance for researchers. Most notably, FluNexus offers the visualization of influenza A virus antigenic evolution, providing intuitive insights into its antigenic dynamics. Specially, FluNexus proposes a novel manifold-based method for positioning antigens and antisera, generating accurate antigenic cartographies even with sparse HI data. By alleviating the programming burden on biologists, FluNexus supports more informed decision-making in vaccine strain selection and strengthens surveillance and pandemic preparedness.
Highlights
FluNexus features a data preprocessing module for HA1 and HI data spanning the H1, H3, and H5 subtypes.
FluNexus facilitates online antigenic prediction utilizing ten state-of-the-art antigenic prediction tools, and offers practical guidance based on a comparative evaluation of their performance.
FluNexus provides a visualization module for mapping antigenic evolution of influenza A viruses, incorporating a novel manifold-based method for antigenic cartography.
Competing Interest Statement
The authors have declared no competing interest.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.