End-to-end metagenome disease classification using pre-trained genomic LLM embeddings
Gut microbiome profiling is emerging as a powerful tool for disease prediction, yet current methods depend on taxonomic annotation pipelines that rely on incomplete reference databases and discard the rich biological information embedded in raw sequencing reads. Here we introduce MetagenBERT, an annotation-free framework that generates metagenome-level disease representations directly from raw DNA reads using pre-trained genomic large language models (gLLMs). Raw reads are embedded by DNABERT-2 or DNABERT-MS, then aggregated into sample-level cluster-abundance vectors via FAISS-accelerated K-means clustering, producing a structured, comparable representation of each metagenome without any taxonomic annotation or reference database. We evaluated MetagenBERT on five widely used benchmark gut microbiome cohorts spanning liver cirrhosis (n = 232), type 2 diabetes (n = 344), inflammatory bowel disease (n = 110), colorectal cancer (n = 121), and obesity (n = 253). Combining cluster and species abundances further improves prediction in most settings, confirming that the two representations capture complementary biological information. Strikingly, competitive performance is maintained using as few as 5–10% of reads per sample, revealing substantial sequence-level redundancy and enabling large reductions in computational cost. Cross-cohort transfer experiments, in which clustering was pre-trained on the MetaCardis cohort (n = 894) and applied to unseen disease phenotypes, preserved predictive signal for several conditions, providing proof of concept for foundation metagenome representations. These results demonstrate that disease-relevant microbiome signals can be extracted directly from raw DNA sequences at scale, opening new avenues for annotation-free, reference-independent microbiome analysis with potential applications in precision medicine.
Gut microbiome profiling is emerging as a powerful tool for disease prediction, yet current methods depend on taxonomic annotation pipelines that rely on incomplete reference databases and discard the rich biological information embedded in raw sequencing reads. Here we introduce MetagenBERT, an annotation-free framework that generates metagenome-level disease representations directly from raw DNA reads using pre-trained genomic large language models (gLLMs). Raw reads are embedded by DNABERT-2 or DNABERT-MS, then aggregated into sample-level cluster-abundance vectors via FAISS-accelerated K-means clustering, producing a structured, comparable representation of each metagenome without any taxonomic annotation or reference database. We evaluated MetagenBERT on five widely used benchmark gut microbiome cohorts spanning liver cirrhosis (n = 232), type 2 diabetes (n = 344), inflammatory bowel disease (n = 110), colorectal cancer (n = 121), and obesity (n = 253). Combining cluster and species abundances further improves prediction in most settings, confirming that the two representations capture complementary biological information. Strikingly, competitive performance is maintained using as few as 5–10% of reads per sample, revealing substantial sequence-level redundancy and enabling large reductions in computational cost. Cross-cohort transfer experiments, in which clustering was pre-trained on the MetaCardis cohort (n = 894) and applied to unseen disease phenotypes, preserved predictive signal for several conditions, providing proof of concept for foundation metagenome representations. These results demonstrate that disease-relevant microbiome signals can be extracted directly from raw DNA sequences at scale, opening new avenues for annotation-free, reference-independent microbiome analysis with potential applications in precision medicine.
Institut de Recherche pour le Developpement