MAF Analysis AI Workflow

This repository provides an AI-powered workflow for analyzing Mutation Annotation Format (MAF) files. It uses the CrewAI framework to orchestrate tasks such as summarizing MAF files, performing somatic interaction analysis, identifying drug-gene interactions, and generating a comprehensive Markdown report.

Features

• MAF Summarization: Summarizes the key details of the MAF file.
• Somatic Interaction Analysis: Identifies significant somatic interactions.
• Drug-Gene Interaction Identification: Finds potential therapeutic targets.
• Comprehensive Report Generation: Combines all outputs into a Markdown report with tables and icons.

Requirements

• Python 3.8 or higher
• Required Python packages (see pyproject.toml for details)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/maf_ai.git
   cd maf_ai

2. Install pip-tools and uv globally if not already installed:

   pip install pip-tools uv

3. Create a virtual environment and activate it:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

4. Install dependencies using uv:

   uv install

   This will automatically resolve and install all dependencies specified in pyproject.toml

5. Set up the environment variables:

   1. Create a .env file in the root directory.
   2. Add your OpenAI API key:

    OPENAI_API_KEY=your_openai_api_key

   Replace your_api_key with your actual CrewAI API key.

Usage

Run the main.py script to analyze a MAF file and generate a Markdown report:

python main.py --maf_path path/to/your/maf_file.maf

Replace path/to/your/maf_file.maf with the actual path to your MAF file.

Command Line Arguments

• --maf-file-path: Path to the MAF file to analyze.
• --instruction: Natural language instruction for the analysis.
• --verbose: Enable verbose output.
• --output-file: Path to save the generated Markdown report (default: maf_analysis_report.md).

Example

python main.py --maf-file-path example.maf --instruction "Analyze the MAF file and summarize the key findings." --verbose

This will analyze the specified MAF file, summarize its contents, and generate a Markdown report with the findings.

Output

The script generates a Markdown report summarizing the analysis results. Example sections include:

• MAF Summary
• Somatic Interactions
• Drug-Gene Interactions
• Conclusion

The report is saved to the specified output file.

Example Output

The generated Markdown report includes:

• MAF Summary: A summary of the MAF file, including the number of mutations, genes involved, and other relevant statistics.

Number of Samples: 10129
Number of Genes: 414
Variant Classifications: {'Missense_Mutation': 55556, 'Nonsense_Mutation': 7936, ...}

• Somatic Interactions: A table of significant somatic interactions, including gene pairs and interaction scores.

| Gene A | Gene B | Interaction Score |
|--------|--------|------------------|
| TP53   | KRAS   | 0.85             |
| EGFR   | PIK3CA | 0.78             |

• Drug-Gene Interactions: A table of potential drug-gene interactions, including drug names and associated genes.

| Drug Name | Gene Name | Interaction Type |
|-----------|-----------|------------------|
| DrugA     | TP53      | Inhibitor        |
| DrugB     | KRAS      | Agonist          |

• Conclusion: This report summarizes the results of the MAF analysis, including the MAF file summary, somatic interaction analysis, and drug-gene interactions. The findings provide valuable insights into potential therapeutic targets and their clinical relevance..

MAF Summary

Project Structure

maf_ai/
├── [main.py]()                     # Entry point for the workflow
├── maf_tools/                  # Tools for MAF analysis
│   ├── maf_summarizer.py       # Summarizes MAF files
│   ├── somatic_interactions.py # Performs somatic interaction analysis
│   ├── drug_gene_interactions.py # Identifies drug-gene interactions
│   ├── natural_language_parser.py # Parses natural language instructions
│   ├── task_delegator.py       # Delegates tasks to agents
├── requirements.txt            # Python dependencies (auto-generated by pip-tools)
├── [pyproject.toml]()              # Dependency management configuration
├── .env                        # Environment variables (not included in repo)
└── [README.md]()                   # Project documentation

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the AGPL License. See the LICENSE file for details.