A-G.19: Read-Level Classification of HPV Viral Metagenomic Data Using Transformer Architectures
Motivation: Accurate detection of viral reads within overwhelming human and bacterial background is essential for viral metagenomics, outbreak surveillance, and precision diagnostics. This is also relevant in cancer and, more broadly, in shotgun sequencing workflows, where accurate read-level discrimination can improve downstream genotyping, assembly, and interpretation. Alignment-free tools such as Kraken-2 are widely
used, but their reliance on exact or near-exact k-mer matches may reduce sensitivity to divergent or low-abundance viral reads. Transformer encoders provide contextual nucleotide representations that may overcome these limitations, yet their performance on realistic HPV-scale short reads remains poorly explored. Results: We compared two viral transformers, ViBE and XVir, against Kraken-2 on 150-bp Illumina-like reads simulated from 184 viral genomes, 147 bacterial genomes, and 62 human assemblies, using human papillomavirus (HPV) as the pathogen of interest. Across four tasks (Virus vs Human, Virus vs Bacterial, Human vs Bacterial, and Virus vs Human vs Bacterial), ViBE consistently generated the most informative embedding space and the best downstream classification, achieving 95–98% balanced accuracy in binary tasks and 91–92% in the three-class setting, often with only the top 100 embedding features. XVir, despite its HPV-focused design, performed well
mainly in Virus vs Human discrimination and generalized less effectively to bacterial background. Kraken-2 remained strongest in Human vs Bacterial separation but showed lower performance on HPV-centered tasks. Availability and implementation: https://github.com/simoRancati/single-read-viral-transformers-Read-Level-Classification-of-Viral-Metagenomic-Data-Using-Transforme
Co-authors: Simone Rancati, Sakshi Pandey, Micheal Sy, Pablo Arozarena Donelli, Giovanna Nicora, Conrad Testagrose, Christina Boucher, Riccardo Bellazzi, Marco Salemi, Enea Parimbelli
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