A-B.09: VPF-Class 2.0: a taxonomy-centered framework for automatic viral classification
Joan Carles Pons
Universitat de les Illes Balears
Merce Llabres
Universitat de les Illes Balears
Keywords
viral taxonomy, ICTV, VPF-Class, supervised classification, neural networks, virome analysis
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The rapid expansion of viral sequence data demands taxonomic classifiers that are scalable, aligned with ICTV updates, and capable of providing interpretable evidence. We present VPF-Class 2.0, an updated successor to VPF-Class designed to meet these challenges. While retaining marker-driven protein domain detection, the new version replaces traditional rule-based voting with a lightweight supervised model based on per-genome marker-composition features.
In controlled benchmarks, VPF-Class 2.0 achieves near-perfect family-level performance and strong genus-level accuracy, significantly increasing confident annotation coverage. Under practical confidence thresholds, its performance matches or exceeds representative tools within shared taxonomic scopes. Beyond accuracy, we introduce an interpretability study that relates classification errors to the genus specificity of activated markers, offering deeper insights into viral diversity. Finally, we demonstrate the tool's applicability on large real-world viromes, showing consistent labeling and substantial agreement with graph-based classifications. This highlight presentation will showcase how VPF-Class 2.0 provides a robust, interpretable, and scalable solution for the next generation of viral metagenomics.
Co-authors: Joan Carles Pons Mayol, Maria de La Merce Llabres Segura, Mateus B Fiamenghi, Nikos C Kyrpides
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