AlphaGenome MCP Server

An MCP server that provides natural language access to Google DeepMind's AlphaGenome for regulatory genomics analysis and variant effect prediction.

Features

• Variant Effect Prediction: Analyze regulatory impacts of genetic variants across 11 molecular modalities (RNA-seq, ChIP-seq, ATAC-seq, splicing, etc.)
• Regulatory Element Discovery: Identify promoters, enhancers, and transcription factor binding sites in genomic regions
• Batch Variant Scoring: Prioritize multiple variants by regulatory impact for GWAS and sequencing studies
• Natural Language Interface: Query variants using rsIDs or genomic coordinates without coding
• Multi-Modal Analysis: Unified predictions for gene expression, chromatin accessibility, TF binding, and 3D chromatin structure

Tools

predict_variant_effect

Predicts the regulatory impact of a single genetic variant.

Inputs:

• chromosome (string): Chromosome name (chr1-chr22, chrX, chrY)
• position (number): Genomic position (1-based)
• ref (string): Reference allele (A/T/G/C)
• alt (string): Alternate allele (A/T/G/C)
• tissue_type (string, optional): Tissue context (UBERON term, e.g., "UBERON:0000955" for brain)
• output_types (array, optional): Specific modalities to analyze

Example:

"Analyze the regulatory impact of chr19:44908684T>C in brain tissue"

analyze_region

Identifies regulatory elements in a genomic region.

Inputs:

• chromosome (string): Chromosome name
• start (number): Start position (1-based)
• end (number): End position
• analysis_types (array, optional): Element types to find (promoter, enhancer, etc.)
• resolution (string, optional): "base" (1bp) or "window" (128bp)

Example:

"Find enhancers in chr11:5225464-5227071"

batch_score_variants

Scores and ranks multiple variants by regulatory impact.

Inputs:

• variants (array): List of variants with chr, pos, ref, alt
• scoring_metric (string): Metric for ranking (rna_seq, splice, regulatory_impact, combined)
• top_n (number, optional): Number of top variants to return
• include_interpretation (boolean, optional): Include clinical interpretation

Example:

"Score these variants by splicing impact: chr7:117199563C>T, chr2:127892810G>A"

Installation

Requirements

• Node.js ≥18.0.0
• Python ≥3.8 with alphagenome and numpy
• AlphaGenome API key (request here)

Quick Start

# Install Python dependencies
pip install alphagenome numpy

# Add to Claude Desktop
claude mcp add alphagenome -- npx -y @jolab/alphagenome-mcp@latest --api-key YOUR_API_KEY

Configuration

Usage with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "alphagenome": {
      "command": "npx",
      "args": ["-y", "@jolab/alphagenome-mcp@latest"],
      "env": {
        "ALPHAGENOME_API_KEY": "your-api-key-here"
      }
    }
  }
}

Or use command-line argument:

{
  "mcpServers": {
    "alphagenome": {
      "command": "npx",
      "args": [
        "-y",
        "@jolab/alphagenome-mcp@latest",
        "--api-key",
        "your-api-key-here"
      ]
    }
  }
}

Verification

Test the installation:

"Analyze chr19:44908684T>C with AlphaGenome"

Expected: Detailed regulatory impact report within 30-60 seconds.

Usage Examples

Basic Analysis

Single Variant:

"What is the regulatory impact of rs429358?"

Specific Tissue:

"Analyze chr6:41129252C>T in brain tissue"

Custom Modalities:

"Show only RNA-seq and splicing effects for chr2:127892810G>A"

Advanced Queries

Region Exploration:

"Find all regulatory elements in the APOE gene region"

Variant Prioritization:

"Rank these 10 variants by their impact on gene expression"

Cross-Tissue Comparison:

"Compare the effect of this variant in brain vs liver"

Mechanistic Investigation:

"Which transcription factors are affected by rs744373?"

Use Cases

• Post-GWAS Analysis: Prioritize GWAS hits by functional impact
• Clinical Interpretation: Assess pathogenicity of VUS (variants of uncertain significance)
• Drug Target Discovery: Identify regulatory variants affecting target genes
• Synthetic Biology: Design tissue-specific regulatory elements
• Evolutionary Genomics: Analyze regulatory changes across species

Development

Build from Source

git clone https://github.com/taehojo/alphagenome-mcp.git
cd alphagenome-mcp
npm install
pip install -r requirements.txt
npm run build

Project Structure

src/
├── index.ts              # MCP server
├── alphagenome-client.ts # API client
├── tools.ts              # Tool definitions
└── utils/                # Validation & formatting
scripts/
└── alphagenome_bridge.py # Python bridge

Testing

npm run lint
npm run typecheck
npm run build

Architecture

Claude Desktop → MCP Server (TypeScript) → Python Bridge → AlphaGenome API

The server uses a Python subprocess bridge to interface with AlphaGenome's Python-only SDK.

Performance

• First call: 30-60 seconds (initialization)
• Subsequent calls: 5-15 seconds
• Recommended: <1000 variants per session
• Modalities: 11 (RNA-seq, CAGE, PRO-cap, splice sites, DNase, ATAC, histone mods, TF binding, contact maps)
• Resolution: Single base-pair for most modalities

Limitations

• Requires active internet and API access
• InDels and structural variants not fully supported
• Accuracy decreases for regulatory elements >100kb from TSS
• Human and mouse genomes only
• Research use only (not validated for clinical diagnostics)

Citation

@software{jo2025alphagenome_mcp,
  author = {Jo, Taeho},
  title = {AlphaGenome MCP Server},
  year = {2025},
  url = {https://github.com/taehojo/alphagenome-mcp},
  version = {0.1.5}
}

AlphaGenome:

@article{avsec2025alphagenome,
  title = {AlphaGenome: Unified prediction of variant effects},
  author = {Avsec, Žiga and Latysheva, Natasha and Cheng, Jun and others},
  journal = {bioRxiv},
  year = {2025},
  doi = {10.1101/2025.06.27.600757}
}

License

MIT License - Copyright (c) 2025 Taeho Jo

Links

• npm: https://www.npmjs.com/package/@jolab/alphagenome-mcp
• GitHub: https://github.com/taehojo/alphagenome-mcp
• Issues: https://github.com/taehojo/alphagenome-mcp/issues
• AlphaGenome: https://deepmind.google/discover/blog/alphagenome/
• Model Context Protocol: https://modelcontextprotocol.io/