MCP 服务器

AlphaGenome MCP Server

Enables AI-powered genomic variant analysis including variant impact prediction, regulatory element discovery, and batch variant scoring. Currently operates in mock mode as a proof-of-concept awaiting the public release of Google DeepMind's AlphaGenome API.

README

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}
}