References and integrates with multiple DROMA project repositories including DROMA.R and DROMA.Set packages for cancer pharmacogenomics analysis functionality.
Integrates with DROMA R packages through Python's rpy2 library to enable natural language analysis of drug-omics associations, molecular profiles, and treatment response data with automatic normalization and caching.
Provides access to DROMA (Drug Response Omics association MAp) SQLite database for querying cancer pharmacogenomics data, including sample annotations, drug information, molecular profiles, and treatment response data across multiple research projects like CCLE and gCSI.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@DROMA MCP Serveranalyze the association between cisplatin and TP53 gene expression in the CCLE dataset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
DROMA MCP Server
A Model Context Protocol (MCP) server for DROMA (Drug Response Omics association MAp) - enabling natural language interactions with drug-omics association analysis.
🚀 Overview
DROMA MCP Server bridges the gap between AI assistants and cancer pharmacogenomics analysis by providing a natural language interface to the DROMA.R and DROMA.Set packages.
It is a part of DROMA project. Visit the official DROMA website for comprehensive documentation and interactive examples.
Key Features
🔗 Natural Language Interface: Ask questions about drug-omics associations in plain English
📊 Dataset Management: Load and manage DROMA datasets (CCLE, gCSI, etc.) in memory
📈 Data Loading & Normalization: Load molecular profiles and treatment response data with automatic z-score normalization
🗂️ Multi-Project Support: Seamlessly work with data across multiple research projects
💾 Smart Caching: Efficient data caching with metadata tracking for faster access
📤 Data Export: Export analysis results to various formats (CSV, Excel, JSON)
⚡ Multi-Modal Support: Works with various transport protocols (STDIO, HTTP, SSE)
🔄 R Integration: Seamless integration with existing DROMA R packages via rpy2
📦 Installation
Prerequisites
Python 3.10+
R 4.0+ with DROMA.Set and DROMA.R packages
DROMA SQLite database
FastMCP 2.13+ (automatically installed)
Install via pip
Development Installation
R Dependencies
Ensure you have the DROMA R packages installed:
🚀 Quick Start
1. Start the Server
2. MCP Client Configuration
Add to your MCP client configuration:
🖥️ CLI Commands
Command | Description |
| Start the MCP server |
| Test configuration and dependencies |
| Validate complete setup |
| Export MCP client configuration |
For detailed help: droma-mcp --help or droma-mcp run --help
🛠️ Available Tools
Dataset Management
load_dataset: Load DROMA datasets (CCLE, gCSI, etc.) into memory from databaselist_loaded_datasets: Show which datasets are currently loaded in memoryset_active_dataset: Set the active dataset for subsequent operationsunload_dataset: Remove datasets from memory to free up resources
Data Loading & Analysis
load_molecular_profiles_normalized: Load molecular profiles (mRNA, CNV, methylation, etc.) with z-score normalizationload_treatment_response_normalized: Load drug response data with normalizationload_multi_project_molecular_profiles_normalized: Load data across multiple projectsload_multi_project_treatment_response_normalized: Load treatment response across projectscheck_zscore_normalization: Verify normalization status of cached dataget_cached_data_info: Get information about cached datasetsexport_cached_data: Export data to CSV/Excel/JSON formats
Database Query & Exploration
get_droma_annotation: Retrieve sample or drug annotation data from the databaselist_droma_samples: List all available samples for a project with filtering optionslist_droma_features: List all available features (genes, drugs) for a project and data typelist_droma_projects: List all projects available in the DROMA database
Visualization & Analysis
plot_drug_sensitivity_rank: Create drug sensitivity rank plotsanalyze_drug_omic_pair: Analyze associations between drugs and omic features
💬 Example Usage
Essential Workflow
⚠️ Important: Before using any data loading functions, you must first load the dataset into memory:
"Load the CCLE dataset from the database and set it as active"
"Load mRNA expression data for ABCB1 gene from the CCLE dataset with z-score normalization"
"List all projects available in the DROMA database"
"Show me all available samples for the gCSI project that have mRNA data"
🔧 Configuration
Environment Variables
DROMA_DB_PATH: Default path to DROMA SQLite databaseR_LIBS: Path to R librariesDROMA_MCP_MODULE: Server module to load (all,data_loading,database_query,dataset_management,visualization,analysis)DROMA_MCP_VERBOSE: Enable verbose logging
🔧 Troubleshooting
Common Issues
Import Errors: Run droma-mcp validate to check dependencies
R Integration Issues: Ensure R and DROMA packages are installed correctly
Database Connection: Verify database path with droma-mcp test --db-path path/to/droma.db
For more help, see the official DROMA website or open an issue on GitHub.
🤝 Contributing
Contributions are welcome! Please see our Contributing Guidelines for details.
📄 License
This project is licensed under the MPL-2 License - see the LICENSE file for details.
🔗 Related Projects
DROMA - Main DROMA project
DROMA.Set - R package for data management
DROMA.R - R package for analysis functions
FastMCP - Python framework for MCP servers
Model Context Protocol - Open standard for AI tool integration
📞 Support
Issues: GitHub Issues
Discussions: GitHub Discussions
Email: Contact DROMA Team
Citation
If you use DROMA MCP in your research, please cite:
DROMA MCP - Bridging AI and Cancer Pharmacogenomics 🧬💊🤖