TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models

TaxDistill introduces a knowledge distillation framework using GenomeOcean, a 500M-parameter genomic foundation model, to improve metagenomic taxonomic annotation by reducing label noise from sequence similarity tools. The approach achieves significant performance gains, improving F1 scores by 23.3% on gastrointestinal datasets compared to traditional methods.

TaxDistill addresses a critical bottleneck in metagenomic analysis—the inherent noise introduced when training datasets rely on imperfect similarity-based labels. Traditional taxonomic annotation methods struggle with incomplete reference databases and microbial diversity, making downstream classification unreliable. The research introduces a teacher-student distillation paradigm where GenomeOcean, a large genomic foundation model, extracts deep semantic features and generates high-confidence soft labels that guide a smaller student network, effectively filtering out label noise from initial retrieval tools.

This work fits within a broader trend of applying large foundation models to specialized biological domains, similar to how large language models have transformed NLP. The use of knowledge distillation specifically targets a practical pain point—most organizations cannot afford to train or deploy 500M-parameter models in production environments. By distilling knowledge into lightweight student networks, TaxDistill bridges the gap between state-of-the-art performance and practical deployment constraints.

For computational biology and bioinformatics teams, this represents a substantial performance improvement without proportional computational overhead. The 23.3% F1-score improvement on gastrointestinal samples demonstrates real-world applicability across diverse microbiome analysis scenarios. Researchers relying on metagenomic classification for clinical diagnostics, environmental monitoring, or genomic research can expect more accurate taxonomic assignments with reduced false positives.

Future developments will likely focus on applying similar distillation frameworks to other genomic tasks and determining whether foundation model pretraining on larger genomic datasets further improves performance. The research suggests foundation models may offer significant advantages in handling biological sequence complexity beyond similarity-based approaches.

• →TaxDistill uses knowledge distillation from a 500M-parameter genomic foundation model to improve metagenomic taxonomic annotation accuracy.
• →The framework achieves 23.3% F1-score improvement on gastrointestinal datasets compared to MMseqs2 baseline methods.
• →Knowledge distillation effectively reduces label noise introduced by initial sequence similarity retrieval tools during training.
• →Lightweight student networks enable practical deployment while maintaining performance gains from large teacher models.
• →The approach outperforms existing Taxometer baselines across seven diverse CAMI2 benchmark datasets.