rettxmutation 0.1.12

Extract Rett Syndrome mutations from genetic diagnosis report

RettX Mutation Analysis Library

A Python library for extracting and analyzing MECP2 mutations from genetic documents using Azure AI services. Designed for medical genetics research, clinical applications, and bioinformatics workflows focused on Rett Syndrome.

🚀 Quick Start

Installation

pip install rettxmutation

Basic Usage

from rettxmutation import RettxServices, DefaultConfig

# Initialize configuration (loads from environment variables)
config = DefaultConfig()

# Create services
services = RettxServices(config)

# Process a genetic document
with open('genetic_report.pdf', 'rb') as file_stream:
    # Extract text using OCR
    document = services.ocr_service.extract_and_process_text(file_stream)
    
    # Detect MECP2-related keywords and mutations
    keyword_collection = services.keyword_detector_service.detect_keywords(document)
    
    # Get structured mutations with confidence scores
    mutations = keyword_collection.get_gene_mutations()
    
    for mutation in mutations:
        print(f"Gene: {mutation.primary_transcript.gene_id}")
        print(f"HGVS: {mutation.primary_transcript.hgvs_transcript_variant}")
        print(f"Protein: {mutation.primary_transcript.protein_consequence_tlr}")

✨ Key Features

• 🔍 Multi-Format Document Processing: PDF, PNG, JPG support with intelligent OCR
• 🧬 HGVS Validation: Built-in mutalyzer integration for mutation validation
• 🤖 AI-Powered Extraction: Azure OpenAI and Semantic Kernel for intelligent text analysis
• 📊 Confidence Scoring: All results include confidence metrics for quality assessment
• 🎯 MECP2 Specialization: Optimized for Rett Syndrome genetic analysis
• ⚡ Production Ready: Type-safe Pydantic v2 models with comprehensive error handling
• 🔄 External API Integration: Ensembl.org enrichment with retry mechanisms
• 🏗️ Modular Architecture: Clean separation of concerns with dependency injection

🛠️ Requirements

Python Version

• Python 3.8 or higher

Azure Services

You'll need access to these Azure services:

• Azure Form Recognizer (Document Intelligence) - for OCR
• Azure OpenAI - for mutation extraction and text analysis
• Azure Cognitive Services (Text Analytics) - for medical text processing
• Azure AI Search (optional) - for enhanced keyword detection

Environment Variables

# Required
RETTX_DOCUMENT_ANALYSIS_ENDPOINT=https://your-form-recognizer.cognitiveservices.azure.com/
RETTX_DOCUMENT_ANALYSIS_KEY=your-form-recognizer-key

RETTX_OPENAI_ENDPOINT=https://your-openai.openai.azure.com/
RETTX_OPENAI_KEY=your-openai-key
RETTX_OPENAI_MODEL_NAME=gpt-4
RETTX_OPENAI_MODEL_VERSION=2024-02-01

RETTX_COGNITIVE_SERVICES_ENDPOINT=https://your-cognitive-services.cognitiveservices.azure.com/
RETTX_COGNITIVE_SERVICES_KEY=your-cognitive-services-key

# Optional (for enhanced features)
RETTX_AI_SEARCH_SERVICE=your-search-service
RETTX_AI_SEARCH_API_KEY=your-search-key
RETTX_AI_SEARCH_INDEX_NAME=your-index-name

📋 Processing Workflow

1. Document Input: Accept PDF or image files
2. OCR Processing: Extract text using Azure Form Recognizer
3. Text Normalization: Clean and standardize extracted text
4. Keyword Detection: Multi-layer detection (regex + AI + search)
5. Mutation Extraction: AI-powered identification using Semantic Kernel
6. HGVS Validation: Validate mutations using mutalyzer parser
7. Data Enrichment: Query Ensembl.org for additional mutation data
8. Structured Output: Return validated mutations with confidence scores

💻 Advanced Usage

Async Mutation Validation

import asyncio
from rettxmutation.models.gene_models import GeneMutation, TranscriptMutation
from rettxmutation.services.mutation_validator import MutationValidator

async def validate_mutations():
    config = DefaultConfig()
    validator = MutationValidator(config)
    
    # Create mutation object
    mutation = GeneMutation(
        variant_type="SNV",
        primary_transcript=TranscriptMutation(
            gene_id="MECP2",
            transcript_id="NM_004992.4",
            hgvs_transcript_variant="NM_004992.4:c.916C>T",
            protein_consequence_tlr="NP_004983.1:p.(Arg306Cys)"
        )
    )
    
    # Validate with external services
    validation_result = await validator.validate_mutations([mutation])
    return validation_result

# Run async function
result = asyncio.run(validate_mutations())

Custom Configuration

from rettxmutation.config import RettxConfig

class CustomConfig:
    """Custom configuration implementation"""
    LOG_LEVEL = "DEBUG"
    ENVIRONMENT = "production"
    RETTX_OPENAI_MODEL_NAME = "gpt-4-turbo"
    # ... other config values

services = RettxServices(CustomConfig())

🎯 Use Cases

• 🏥 Clinical Genetics: Process genetic reports for patient diagnosis
• 🔬 Research: Analyze genetic data for Rett Syndrome studies
• 📊 Patient Registries: Populate genetic databases systematically
• 🤖 Bioinformatics Pipelines: Integrate with existing analysis workflows
• 📱 Clinical Applications: Build genetic analysis tools and dashboards

🔧 Error Handling & Reliability

• Exponential Backoff: Automatic retry for external API calls (up to 5 attempts)
• Graceful Degradation: Continue processing when optional services are unavailable
• Comprehensive Logging: Detailed logging for debugging and monitoring
• Type Safety: Pydantic v2 models ensure data validation at runtime
• Connection Pooling: Efficient Azure service client management

🤝 Contributing

We welcome contributions! Please see our GitHub repository for:

• Issue reporting
• Feature requests
• Pull request guidelines
• Development setup instructions

📄 License

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

🆘 Support

• Issues: GitHub Issues
• Documentation: API Documentation
• Contact: [e-mail]

🔮 Roadmap

• Multi-Gene Support: Extend beyond MECP2 to other genetic conditions
• Enhanced Image Processing: Advanced OCR for handwritten documents
• Multilingual Support: Process documents in multiple languages
• Real-time Processing: WebSocket support for live document analysis
• Cloud Deployment: Docker containers and Azure deployment templates