GWAS Bioinformatics MCP Server

🧬 GWAS-MCP: Bioinformatics MCP Server

[PyPI version] [License: MIT] [MCP]

A powerful Model Context Protocol (MCP) server for GWAS and bioinformatics research. Seamlessly integrates with Claude Desktop and other MCP clients to provide AI-powered access to major biological databases.

[30+ Tools] [12+ Databases] [Python 3.10+]

✨ Features

🔬 Protein & Gene Lookup

• UniProt - Search proteins by name, gene, or ID
• Ensembl - Gene information and variant details
• NCBI Gene - Comprehensive gene database

🧪 Clinical & Variants

• ClinVar - Clinical variant interpretations (pathogenic/benign)
• GWAS Catalog - Genome-wide association studies
• GTEx - Expression quantitative trait loci (eQTL)

🔗 Protein Interactions & Networks

• STRING - Protein-protein interactions
• InterPro - Protein domains and families

🏗️ Structures & Pathways

• AlphaFold - AI-predicted protein structures
• PDB - Experimental 3D structures
• KEGG - Metabolic and signaling pathways

💊 Drug Discovery

• Open Targets - Drug target validation & disease associations
• PharmGKB - Pharmacogenomics & drug-gene interactions

🏥 Genetic Diseases

• OMIM - Online Mendelian Inheritance in Man

🚀 Quick Start

Installation

pip install gwas-mcp

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{  "mcpServers": {    "gwas-bioinformatics": {      "command": "python",      "args": ["-m", "gwas_mcp.server"]    }  }}

Config file location:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Restart Claude Desktop

After adding the configuration, restart Claude Desktop to load the MCP server.

🛠️ Available Tools

Protein & Gene Tools

Clinical & Variant Tools

Protein Interaction Tools

Structure & Pathway Tools

Drug Discovery Tools

Genetic Disease Tools

💬 Example Prompts

Once configured, ask Claude naturally:

Protein & Gene Queries

"Get information about the BRCA1 gene"

"Search UniProt for hemoglobin"

"What protein has UniProt ID P53_HUMAN?"

Clinical Variants

"Is the BRCA1 variant rs80357906 pathogenic?"

"Search ClinVar for TP53 variants"

Protein Interactions

"What proteins interact with TP53?"

"Find functional enrichment for BRCA1, ATM, and CHEK2"

Structures & Pathways

"Get the AlphaFold structure for TP53"

"What pathways is BRCA1 involved in?"

"Search PDB for insulin structures"

Drug Discovery

"What drugs target EGFR?"

"What diseases is BRAF associated with?"

Genetic Diseases

"Search OMIM for cystic fibrosis"

"What diseases are linked to the CFTR gene?"

⚡ Performance Features

• Smart Caching - API responses cached for 1 hour to improve speed
• Async Operations - All API calls are non-blocking
• Error Handling - Graceful handling of API failures

🔧 Development

From Source

# Clone the repositorygit clone https://github.com/zaeyasa/gwas-mcp.gitcd gwas-mcp
# Install dependenciespip install -e .
# Run the serverpython -m gwas_mcp.server

Project Structure

gwas-mcp/├── src/│   └── gwas_mcp/│       ├── server.py           # Main MCP server│       ├── tools/│       │   ├── protein_tools.py     # UniProt, NCBI, Ensembl│       │   ├── clinical_tools.py    # ClinVar, STRING│       │   ├── structure_tools.py   # PDB, KEGG, PharmGKB│       │   └── advanced_tools.py    # AlphaFold, Open Targets, OMIM│       └── resources/│           └── db_resources.py      # Database resources├── pyproject.toml├── README.md└── LICENSE

📊 Supported Databases

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Model Context Protocol - The MCP specification
• Anthropic - Claude AI and MCP development
• All the amazing bioinformatics databases that make this possible

📬 Contact

• GitHub: @zaeyasa

Made with ❤️ for the bioinformatics community