Pilgrim, C. | bioRxiv Science

bioRxiv2026

VFB-MCP: Natural-Language Access to Drosophila Neuroscience Grounded by an Expert-Curated Ontology-Led Knowledgebase

Biological databases store curated knowledge that researchers traditionally access through web interfaces or APIs. To move beyond casual browsing requires domain-specific knowledge and expertise to frame the queries necessary to explore this data. This generates barriers to both experienced users trying to integrate wide-ranging sources of information and for new users entering scientific fields undergoing paradigm shifts such as connectomics. A potentially powerful method to lower these barriers is to facilitate natural-language access to these databases by exposing them to large language models (LLMs) via the Model Context Protocol (MCP). Here we implement this for Virtual Fly Brain (VFB), an expert-curated and ontology-backed knowledgebase of Drosophila neuroscience, providing the precision needed to make recently integrated connectome and molecular data accessible. Benchmarked on 30 neuroscience tasks against a bare LLM and a web-search-assisted LLM, the VFB-MCP-equipped LLM produces precise, verifiable and appropriately quantified answers on 25/30 tasks vs 14/30 for web and 2/30 for bare. The MCP advantage is largest for tasks where data quantification is required (89% vs 11% web). This work establishes MCP layered over ontology-backed knowledge graphs as an effective method to improve LLM response quality for neuroscience and connectomics data, promoting accessibility and accelerating the reliable analysis of complex integrated datasets.

neuroscience↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗