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🧬 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.


✨ 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


Related MCP server: GenomeMCP

🚀 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

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


💬 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 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

📊 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


🤝 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


📬 Contact


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