DNA_PARSER Genetic Analysis Application

This Genetic Analysis Application is designed to extract and analyze Single Nucleotide Polymorphisms (SNPs) from genomic data files such as BAM or CRAM, query SNPedia for detailed SNP information, and incorporate user-reported symptoms to provide a personalized genetic health report.

Features

• SNP Extraction: Parses BAM/CRAM files to extract SNP identifiers.
• SNPedia Integration: Fetches genetic variant information from SNPedia.
• Symptom Analysis: Correlates SNPs with user-reported symptoms to suggest potential health implications.
• Machine Learning: Utilizes TensorFlow to predict conditions based on SNP profiles.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.8 or higher
• pip for Python package management

Installation

To install the necessary libraries, run the following command in your terminal:

pip install pysam requests pandas tensorflow scikit-learn

Usage

Follow these steps to use the application:

1. Prepare Your Data: Ensure your genomic data file (BAM or CRAM) is accessible to the script. Replace 'path/to/your/dna/file.bam' in the script with the path to your genomic data file.

2. Running the Script:

   python genetic_analysis.py

3. Input Symptoms: When prompted, enter the symptoms you want to analyze. The script currently simulates this step, so you might want to modify it to accept real-time user input.

4. Review the Output: After running, the script will print correlated SNP-symptom results and the machine learning model's accuracy and loss metrics.

Configuration

To adapt the application to your needs, consider modifying the following sections of the script:

• SNP Extraction Function (extract_snps): Modify the extraction logic based on the specifics of your BAM/CRAM file format.
• API Query Construction: Adjust the query_snpedia function if you need to handle different SNPedia response structures or add error handling.
• Symptom-SNP Correlation Logic: Enhance the correlate_snps_with_symptoms function to match more complex symptom descriptions or a broader range of medical conditions.

Contributing

Contributions to this project are welcome. To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your_feature).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature/your_feature).
5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

This README provides a comprehensive guide to setting up and using your genetic analysis application. Adjust the content as necessary to fit the specific functionalities and configurations of your application.