HYMET: a hybrid metagenomic pipeline for accurate and efficient taxonomic classification

BACKGROUND: Reliable taxonomic classification of metagenomic sequences remains constrained by high mutation rates, fragmented assemblies, and large heterogeneous reference databases. HYMET (Hybrid Metagenomic Tool) was developed to overcome these challenges through a 2-stage hybrid design combining adaptive Mash-based screening with Minimap2 alignment and a coverage-weighted Lowest Common Ancestor classifier. Its sample-adaptive thresholds and on-the-fly reference database construction enable efficient, domain-agnostic classification while maintaining accuracy across divergent genomes. RESULTS: Across 7 CAMI assembly datasets in contig mode, HYMET achieved a mean F1 of 83.89%, with genus-level F1 of 76.75% and species-level F1 of 60.18%, while averaging 115.93 s runtime and a mean peak memory of 6.24 GB. Performance remained stable under mutation rates up to 30% for most domains (F1 $\ge$ 0.8), with viral sequences showing the expected decline (F1 $\approx$ 0.5 at 30%). Read and contig inputs produced nearly identical results when sharing reference caches, and real-world datasets confirmed robustness with the human gut metagenome, which reproduced typical anaerobic profiles, while in the ZymoBIOMICS mock community, HYMET recovered all bacterial members; a further ground-truth evaluation on the ZymoBIOMICS Gut Microbiome Standard (D6331) yielded near-perfect genus-level concordance (Pearson $r = 0.998$, Bray-Curtis $= 0.04$) across bacteria, fungi, and archaea. CONCLUSIONS: HYMET achieves a practical balance of accuracy, efficiency, and scalability for metagenomic classification. Its adaptive candidate selection, alignment-anchored taxonomy, and reproducible reference caching collectively enhance performance across domains. HYMET source code is fully available at https://github.com/ieeta-pt/HYMET.