🧬 GWAS-MCP: Bioinformatics MCP Server

<!-- mcp-name: io.github.zaeyasa/gwas-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.

<p align="center"> <img src="https://img.shields.io/badge/Tools-30+-green" alt="30+ Tools"> <img src="https://img.shields.io/badge/Databases-12+-blue" alt="12+ Databases"> <img src="https://img.shields.io/badge/Python-3.10+-blue" alt="Python 3.10+"> </p>

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✨ 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

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🚀 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.

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🛠️ Available Tools

Protein & Gene Tools

Tool	Description
search_uniprot	Search UniProt by protein name, gene, or ID
get_protein_details	Get detailed protein info (function, domains, GO terms)
search_ncbi_gene	Search NCBI Gene database
search_ensembl_gene	Get gene location and details from Ensembl
get_variant_info	Get SNP/variant info by rsID
get_interpro_domains	Get protein domain information

Clinical & Variant Tools

Tool	Description
search_clinvar	Search ClinVar for clinical variants
get_clinvar_variant	Get clinical interpretation for a variant
annotate_snps	Annotate SNPs with functional consequences
query_gwas_catalog	Query GWAS Catalog for associations
get_eqtl_data	Get eQTL data from GTEx

Protein Interaction Tools

Tool	Description
get_protein_interactions	Find interacting proteins (STRING)
get_interaction_network	Get network between multiple proteins
get_functional_enrichment	Pathway/GO enrichment analysis

Structure & Pathway Tools

Tool	Description
get_alphafold_structure	Get AI-predicted structure
search_alphafold	Search AlphaFold database
search_pdb_structures	Search PDB for 3D structures
get_pdb_structure	Get PDB structure details
search_kegg_pathway	Search KEGG pathways
get_kegg_pathway	Get pathway genes and details
get_gene_pathways	Find pathways for a gene

Drug Discovery Tools

Tool	Description
get_drug_targets	Find drugs targeting a gene (Open Targets)
get_disease_associations	Get disease associations with scores
search_open_targets	Search genes, diseases, or drugs
search_pharmgkb	Search PharmGKB database
get_drug_gene_interactions	Get drug-gene interactions

Genetic Disease Tools

Tool	Description
search_omim	Search OMIM for genetic diseases
get_gene_diseases	Get all diseases for a gene

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💬 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?"

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⚡ 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

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🔧 Development

From Source

# Clone the repository
git clone https://github.com/zaeyasa/gwas-mcp.git
cd gwas-mcp

# Install dependencies
pip install -e .

# Run the server
python -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

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📊 Supported Databases

Database	Type	Description
UniProt	Protein	Protein sequences and annotations
Ensembl	Gene/Variant	Genome browser and variant data
NCBI Gene	Gene	Gene information database
ClinVar	Clinical	Clinical variant interpretations
GWAS Catalog	GWAS	Genome-wide association studies
GTEx	Expression	Expression QTL data
STRING	Interactions	Protein-protein interactions
InterPro	Domains	Protein families and domains
AlphaFold	Structure	AI-predicted structures
PDB	Structure	Experimental 3D structures
KEGG	Pathways	Metabolic and signaling pathways
Open Targets	Drug Discovery	Drug targets and disease associations
PharmGKB	Pharmacogenomics	Drug-gene interactions
OMIM	Diseases	Genetic disease database

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🤝 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

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📄 License

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

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🙏 Acknowledgments

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

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📬 Contact

• GitHub: @zaeyasa

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<p align="center">Made with ❤️ for the bioinformatics community</p>