What happened
Species-resolved profiling of microbial functional genes is important for linking microbial community composition to biological function, but existing functional profiling approaches are not designed to systematically define user-specified genes and resolve their contributing species. We developed Vritra (Versatile gene-guided Reads-identification with Impartial Taxonomic Refinement and Assignment), a framework for constructing gene-specific reference databases for metagenomic and metatranscriptomic data. Vritra expands and refines the UniRef-based sequence space for user-specified target genes using sequence-similarity network connectivity and functional annotations, retains related homologs as decoys to reduce assignment ambiguity, and links the resulting sequences to standardized microbial taxonomy. Across genes involved in oxalate, urate, and bile acid metabolism, Vritra recovered >97% of sequences represented by established annotation resources for most evaluated genes while substantially expanding the represented sequence space. In real microbiome datasets, the expanded references increased recovery of target-gene reads by up to threefold for poorly annotated genes while maintaining high sequence identity. Application to population-based and publicly available microbiome datasets enabled species-resolved profiling of functional genes and revealed gene-specific associations with microbial taxa, dietary factors, and disease-related phenotypes. Vritra provides a scalable framework for translating continuously expanding sequence resources into gene-specific, species-resolved references for microbiome studies. Update (2026-09-21): A Comparative Benchmark of Biomedical Language Models for Concept Normalization from Real-World Text . Patient-reported and clinically documented narratives often contain informal, fragmented and linguistically heterogeneous expressions, complicating medical concept normalization (MCN) and frequently necessitating pre-processing before terminology mapping. Despite rapid advances in biomedical language modeling, the comparative utility of representation models for MCN and their integration with instruction-tuned LLMs in automated normalization workflows remain underexplored. In this study, we systematically benchmarked 15 general, biomedical and clinical transformer-based representation models together with 12 instruction-tuned LLMs across 12,713 instances from five established datasets: TAC2017_ADR, TwADR-L, TwiMed, CADEC and SMM4H2017. Representation models were evaluated using embedding-based semantic retrieval, whereas instruction-tuned LLMs were assessed as upstream text-correction modules. SapBERT achieved the highest Top-5 accuracy among representation models, reaching 63.8% for SNOMED CT and 58.0% for MedDRA without correction. Qwen 2 Instruct was