The Flu Mutation Explorer is presented, an interactive web application that combines large-scale influenza phylogenies with a manually curated database of reported mammalian adaptation mutations, to enable the exploration and interpretation of IAV genetic variation.
Abstract
A rapid expansion of influenza A virus (IAV) genome sequencing has transformed global surveillance but has also created major challenges for interpreting the biological significance of viral mutations, particularly amino acid replacements associated with host adaptation. Resources have been created to support mutation annotation and phylogenetic analysis, but there is a need for a tool that integrates experimentally derived phenotypic evidence with evolutionary context in a framework suitable for users without prior training in bioinformatics. Here, we present the Flu Mutation Explorer, an interactive web application that combines large-scale influenza phylogenies with a manually curated database of reported mammalian adaptation mutations, to enable the exploration and interpretation of IAV genetic variation. The underlying database comprises over 1.5 million publicly available IAV sequences and over 1000 mutations associated with mammalian adaptation. The Flu Mutation Explorer enables users to query protein sequences, visualise amino acid distributions across viral lineages, examine host-specific conservation patterns, and identify adaptation mutation with links to supporting literature. We include case studies which demonstrate the platform’s use in assessing amino acid conservation at sites of interest and in rapidly identifying candidate mammalian adaptation mutations during the ongoing H5N1 panzootic. By integrating genomic, phylogenetic, and functional information into an intuitive interface, the Flu Mutation Explorer lowers the barriers to interpreting influenza sequences for specialists and non-specialists alike.
This Review critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.
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