plutzer/ProteomicsMCP

ProteomicsMCP

MCP Server for querying CPTAC proteomics data through the Model Context Protocol.

Installation

1. Install Python dependencies:

pip install cptac pandas numpy scipy mcp

2. Clone or download this repository to your local machine.

3. Configure Claude Desktop to use this MCP server:

Edit your Claude Desktop configuration file:

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

Add the following configuration (replace paths with your actual absolute paths):

{
  "mcpServers": {
    "cptac": {
      "command": "/absolute/path/to/python",
      "args": ["/absolute/path/to/ProteomicsMCP/cptac_proteomics.py"],
      "cwd": "/absolute/path/to/ProteomicsMCP"
    }
  }
}

Important: You must use absolute paths for both the Python executable and the script.

Finding Your Python Path

Using a virtual environment (recommended):

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install cptac pandas numpy scipy mcp

# Find the Python executable path
which python  # On Windows: where python

Using conda:

conda create -n proteomics python=3.11
conda activate proteomics
pip install cptac pandas numpy scipy mcp
which python  # On Windows: where python

Example configurations:

Windows with conda:

{
  "mcpServers": {
    "cptac": {
      "command": "C:\\Users\\YourName\\anaconda3\\envs\\proteomics\\python.exe",
      "args": ["C:\\Users\\YourName\\Repos\\ProteomicsMCP\\cptac_proteomics.py"],
      "cwd": "C:\\Users\\YourName\\Repos\\ProteomicsMCP"
    }
  }
}

macOS/Linux with venv:

{
  "mcpServers": {
    "cptac": {
      "command": "/home/yourname/ProteomicsMCP/venv/bin/python",
      "args": ["/home/yourname/ProteomicsMCP/cptac_proteomics.py"],
      "cwd": "/home/yourname/ProteomicsMCP"
    }
  }
}

4. Restart Claude Desktop for the changes to take effect.

Usage

Once configured, the CPTAC MCP server provides the following tools in Claude Desktop:

• get_cancer_types(): List available cancer datasets
• phospho_tumor_vs_normal(cancer, query, normalized): Query phosphoproteomics data with tumor vs normal statistics
  • Returns log2 fold change, p-value, and FDR-adjusted p-value for each phosphosite
  • Performs paired t-tests between tumor and normal samples
• protein_tumor_vs_normal(cancer, query): Query whole-cell proteomics data with tumor vs normal statistics
  • Returns log2 fold change, p-value, and FDR-adjusted p-value for each protein
  • Performs paired t-tests between tumor and normal samples

Example Queries

Ask Claude:

• "What cancer types are available in CPTAC?"
• "Get phosphoproteomics data for AKT1_S473 in breast cancer"
• "Show me all phosphosites for TP53 in lung adenocarcinoma, normalized"
• "Compare AKT1 and EGFR protein levels between tumor and normal in ovarian cancer"
• "What are the most significantly changed phosphosites for MAPK1 in colorectal cancer?"

Supported Cancer Types

• brca - Breast Cancer
• coad - Colon Adenocarcinoma
• hnscc - Head and Neck Squamous Cell Carcinoma
• luad - Lung Adenocarcinoma
• ovarian - Ovarian Cancer
• ccrcc - Clear Cell Renal Cell Carcinoma
• gbm - Glioblastoma
• lscc - Lung Squamous Cell Carcinoma
• pdac - Pancreatic Ductal Adenocarcinoma