🧬 BioBTree v2

A unified biomedical graph database that integrates 50+ primary data sources — genes, proteins, compounds, diseases, pathways, and clinical data — into a single queryable graph with billions of cross-reference edges. Its native MCP server gives LLMs direct access to structured, authoritative biomedical data, complementing their reasoning with reliable identifiers and up-to-date database content.

BRCA1 >> ensembl >> uniprot >> pdb[resolution<2.0]

This finds BRCA1 in Ensembl, maps to UniProt proteins, and returns high-resolution PDB structures — crossing three databases in a single line.

🔗 Try It

The fastest way to experience BioBTree v2 is through MCP. We recommend Claude CLI (tested extensively), though Codex CLI and Gemini CLI also work:

{
  "mcpServers": {
    "biobtree": {
      "type": "http",
      "url": "https://sugi.bio/biobtree/mcp"
    }
  }
}

Once connected, just ask questions in natural language — the AI will query BioBTree automatically:

💊 "What tissues express SCN9A most highly? Are there safety concerns for a Nav1.7 inhibitor?"

🧪 "How many ClinVar variants does BRCA1 have? How many are pathogenic?"

🎯 "What are all the protein targets of Alectinib with IC50 values?"

A REST API is also available for direct programmatic access:

https://sugi.bio/biobtree/api/search?i=BRCA1
https://sugi.bio/biobtree/api/map?i=BRCA1&m=>>ensembl>>uniprot>>chembl_target
https://sugi.bio/biobtree/api/entry?i=P38398&s=uniprot

Q&A

Browse biological questions answered with BioBTree: sugi.bio/biobtree

📖 Documentation

Query syntax, integrated databases, development, and other details check the : docs/ or refer to latest preprint.

For any questions, issues, or collaboration ideas, feel free to create an issue or reach out at tamer.gur07@gmail.com.

📄 Publication

BioBTree v2: Grounding LLM Responses with Large-Scale Structured Biomedical Data

The full preprint manuscript, detailing the graph architecture and comparative LLM use cases is available on Zenodo: https://zenodo.org/records/18962899

BioBTree v1: F1000Research