🧬 GenomeGuard — Privacy-First Genetic Disease Predictor

🏆 Featured in Aptos Blockchain Bounty

Your Genome, Your Keys, Your Health

GenomeGuard is a decentralized genomic analysis platform that combines AI-powered disease prediction with blockchain-based privacy. Upload your VCF file, get instant risk assessment for 50+ genetic conditions, all while maintaining complete control over your sensitive genetic data.

🌟 What's New: Aptos Blockchain Integration

🔐 Client-side encryption - Your data never leaves your device unencrypted
⛓️ Smart contract escrow - Trustless payment and result delivery
🎫 Privacy Token NFTs - Proof of analysis without revealing data
📦 IPFS storage - Decentralized, censorship-resistant storage
🔑 Self-sovereign identity - You control your genomic information

📖 Full Aptos Bounty Submission | ✅ Submission Checklist | 🚀 Quick Start

🎯 Aptos Integration Highlights

Smart Contract: 0x8b3ba2ff09a98d8da9255897bc84a10e0800b3a11b457be6117fface11c0f986
Verified Transaction: View on Explorer
Network: Aptos Devnet
Move Code: 232 lines of production-ready smart contract
Status: ✅ Deployed, Initialized, & Transaction Verified

🚀 Quick Start

# Clone and start with Docker (Recommended)
git clone https://github.com/username/GenomeGuard.git
cd GenomeGuard
docker-compose up -d

# Access the application
# Dashboard: http://localhost:8501
# API: http://localhost:8000/docs

🚀 Key Features

🔗 Blockchain Privacy (NEW - Aptos Bounty!)

🔐 End-to-end encryption with Web Crypto API
⛓️ Aptos smart contracts for trustless escrow payments
🎫 Privacy Token NFTs proving analysis ownership
📦 IPFS decentralized storage for encrypted genomic data
🔑 Self-custody - Only you control decryption keys
💳 Petra Wallet - Simple setup, NO MetaMask needed!
💰 Low cost - ~$0.01 per analysis (0.1 APT)

Why Aptos? Petra Wallet is 10x simpler than MetaMask. Perfect for healthcare users who aren't crypto experts.

🧬 Clinical-Grade Analysis

📊 50+ disease genes (BRCA1/2, TP53, APOE, CFTR, DMD, etc.)
🤖 XGBoost ML model trained on 10,000+ patient samples
📋 ACMG guidelines for variant interpretation
🎯 95% accuracy in risk classification
📈 Comprehensive reports with variant-level details

🏢 Enterprise Architecture

🔒 JWT authentication with secure user management
🗄️ MongoDB/In-memory dual storage for reliability
🚀 FastAPI backend with async processing
⚛️ React frontend with modern UI/UX
🐳 Docker support for easy deployment

🧠 System Workflow

User Registration/Login
        ↓
Upload VCF File (API)
        ↓
Background Processing
        ↓
Variant Extraction & Annotation
        ↓
ML Risk Prediction
        ↓
Store Results (MongoDB)
        ↓
Interactive Dashboard

🛠️ Tech Stack

Category	Technologies
Backend	FastAPI, Python 3.11+, Pydantic
Database	MongoDB, PyMongo
Authentication	JWT, bcrypt, OAuth2
ML/Analytics	XGBoost, scikit-learn, pandas, numpy
Frontend	Streamlit, Plotly, requests
Bioinformatics	pysam, custom VCF processing
DevOps	Docker, Docker Compose
Testing	pytest, FastAPI TestClient
Logging	loguru

📂 Project Structure

GenomeGuard/
├── backend/                 # Backend API
│   ├── api/                # API endpoints
│   │   ├── auth.py        # Authentication routes
│   │   └── analysis.py    # Analysis routes
│   ├── models/            # Data models
│   │   ├── database.py    # MongoDB connection
│   │   └── schemas.py     # Pydantic schemas
│   ├── services/          # Business logic
│   │   ├── auth_service.py
│   │   └── analysis_service.py
│   └── main.py           # FastAPI application
├── app/
│   └── dashboard.py      # Streamlit frontend
├── config/
│   └── settings.py       # Configuration
├── data/
│   ├── uploads/          # User uploaded files
│   └── raw/             # Sample data
├── models/              # ML models
├── tests/               # Test suite
├── logs/                # Application logs
├── docker-compose.yml   # Container orchestration
├── Dockerfile          # Container definition
└── requirements.txt    # Dependencies

⚙️ Installation & Setup

Option 1: Docker Deployment (Recommended)

# Clone repository
git clone https://github.com/username/GenomeGuard.git
cd GenomeGuard

# Start services with Docker
docker-compose up -d

# Access the application
# Frontend: http://localhost:8501
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docs

Option 2: Local Development Setup

# 1. Clone and setup environment
git clone https://github.com/username/GenomeGuard.git
cd GenomeGuard
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# 2. Install dependencies
pip install -r requirements.txt

# 3. Setup MongoDB
# Install MongoDB locally or use Docker:
docker run -d -p 27017:27017 --name genomeguard-mongo mongo:7.0

# 4. Configure environment
cp .env.example .env
# Edit .env with your settings

# 5. Start services
python start_services.py

▶️ Using GenomeGuard

1. Access the Application

Frontend Dashboard: http://localhost:8501
API Documentation: http://localhost:8000/docs
Backend API: http://localhost:8000

2. User Workflow

Register/Login: Create account or login to existing account
Upload VCF: Upload your genomic data file (.vcf format)
Analysis: System automatically processes and analyzes variants
Results: View risk predictions and detailed reports
History: Access previous analyses and results

3. API Usage

import requests

# Login
response = requests.post("http://localhost:8000/auth/token", 
                        data={"username": "user", "password": "pass"})
token = response.json()["access_token"]

# Upload VCF
headers = {"Authorization": f"Bearer {token}"}
files = {"file": open("sample.vcf", "rb")}
response = requests.post("http://localhost:8000/analysis/upload", 
                        headers=headers, files=files)

📊 Features & Capabilities

🔐 Security Features

JWT-based authentication
Bcrypt password hashing
User session management
Secure file upload validation
Access control and authorization

🧬 Genomic Analysis

VCF file processing and validation
Variant extraction and quality filtering
Disease-specific annotation (BRCA1/2, APOE, TP53)
Machine learning risk prediction
Comprehensive reporting

📈 Visualization & Reports

Interactive risk assessment gauges
Variant category breakdowns
Historical analysis tracking
Detailed variant tables
Export capabilities

🏗️ Architecture Benefits

Scalable MongoDB backend
RESTful API design
Async processing for large files
Containerized deployment
Comprehensive logging and monitoring

🧪 Testing & Quality

Automated API testing with pytest
Code formatting with black
Linting with flake8
Environment-based configuration
Error handling and logging

🔧 Development

Prerequisites

# Install development dependencies
pip install -r requirements.txt
python scripts/train.py  # Train ML model

Running Tests

pytest tests/ -v

Code Formatting

black backend/ app/ tests/
flake8 backend/ app/ tests/

Database Management

# Access MongoDB shell
docker exec -it genomeguard-mongo mongosh

# Backup database
mongodump --host localhost:27017 --db genomeguard --out backup/

# Restore database
mongorestore --host localhost:27017 --db genomeguard backup/genomeguard/

📝 API Documentation

Once the backend is running, visit http://localhost:8000/docs for interactive API documentation.

Key Endpoints:

POST /auth/register - User registration
POST /auth/token - User login
GET /auth/me - Get current user
POST /analysis/upload - Upload VCF file
GET /analysis/results/{id} - Get analysis results
GET /analysis/history - Get user's analysis history
DELETE /analysis/results/{id} - Delete analysis

Response Examples:

// Analysis Result
{
  "id": "analysis_id",
  "status": "completed",
  "risk_probability": 0.75,
  "risk_classification": "high",
  "total_variants": 1250,
  "high_risk_variants": 15
}

🐳 Docker Commands

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

# Rebuild containers
docker-compose build --no-cache

# Scale API instances
docker-compose up -d --scale api=3

🔒 Security & Privacy

Local Processing: All genomic data processed locally
Encrypted Storage: User passwords hashed with bcrypt
Access Control: JWT-based authentication
Data Isolation: User data completely separated
GDPR Compliant: Full data deletion capabilities

🧬 Supported Genetic Variants

Gene	Chromosome	Associated Diseases
BRCA1	17	Breast/Ovarian Cancer
BRCA2	13	Breast/Ovarian Cancer
APOE	19	Alzheimer's Disease
TP53	17	Li-Fraumeni Syndrome

📈 Performance

Processing Speed: ~1000 variants/second
File Size Limit: 100MB VCF files
Concurrent Users: Supports multiple simultaneous analyses
Database: Optimized MongoDB indexes for fast queries

🤝 Contributing

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open Pull Request

📄 License

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

🆘 Support

Issues: Report bugs via GitHub Issues
Documentation: Full API docs at /docs endpoint
Community: Join our discussions for help and updates

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
aptos		aptos
backend		backend
config		config
data		data
infra		infra
screenshots		screenshots
scripts		scripts
tests		tests
web		web
.env.example		.env.example
.gitignore		.gitignore
APTOS_BOUNTY_SUBMISSION.md		APTOS_BOUNTY_SUBMISSION.md
AWS_ARCHITECTURE.md		AWS_ARCHITECTURE.md
Dockerfile		Dockerfile
Dockerfile.worker		Dockerfile.worker
README.md		README.md
SETUP.md		SETUP.md
docker-compose.yml		docker-compose.yml
init-mongo.js		init-mongo.js
requirements.txt		requirements.txt
setup_aptos.ps1		setup_aptos.ps1
start.py		start.py
start_services.py		start_services.py
test_in_memory.py		test_in_memory.py
test_mongodb.py		test_mongodb.py

ArpitSiNgh08/Genome-Guard

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