rettxmutation 0.1.8

Extract Rett Syndrome mutations from genetic diagnosis report

rettxmutation - RettX Mutation Analysis Library

Purpose

• Analyze genetic documents systematically to:
  • Extract and identify MECP2 mutations.
  • Normalize mutation data for downstream applications.
• Output structured results with confidence scores for decision-making.

Features

1. Flexible Workflow

With this library you can cover different use cases.

• Batch Processing: Process multiple files in a single run.
• Single File Analysis: Handle individual files, triggered by:
  • File uploads.
  • Scheduled tasks.
  • API calls.
• Input Types:
  • Images (preprocessed to optimize OCR results).
  • PDF documents (direct text extraction).

2. Systematic Workflow

• Preprocessing (for images):
  • Binarization, sharpening, and contrast adjustment.
  • Enhances image quality for better OCR accuracy.
• Text Extraction:
  • OCR applied to extract raw text.
  • Text cleaned to remove artifacts and standardize formatting.
• Keyword Detection:
  • Identify MECP2-related terms and gene variants.
  • Assign confidence scores to detected keywords.
• Summarization and Correction:
  • Generate concise summaries using OpenAI.
  • Validate and correct summaries with Azure Cognitive Services (Text Analytics for Health).
• Mutation Extraction:
  • Extract potential mutations and assign confidence scores.
  • Filter mutations based on user-defined thresholds.
• Data Enrichment:
  • Query Ensembl.org for detailed mutation information.
  • Map mutations to transcripts and protein variants.

3. Integration-Ready Outputs

• Models: Built with Pydantic v2 for seamless data validation.
• Output Formats:
  • JSON (structured data).
  • Objects ready for database storage (e.g., CosmosDB).
• Confidence Scores:
  • Provided as-is for users to interpret and filter based on needs.

Limitations

• Basic Retry Mechanisms:
  • The library includes a retry policy for specific external calls:
    • Ensembl: Retries API requests for fetching variations when encountering:
      • HTTP errors.
      • Connection issues.
      • Timeout errors.
    • OpenAI: Similar retry logic ensures stability in mutation summarization and extraction tasks.
  • Retries are implemented using exponential backoff (up to 5 attempts).
• Error Handling Beyond Retries:
  • If all retry attempts fail, the library does not provide fallback mechanisms.
  • Invalid results or unhandled errors must be managed by the caller.
• MECP2 Priority:
  • Current version focuses exclusively on MECP2 mutations.
  • Extension to other genes or conditions is possible but not yet implemented.

Workflow Summary

1. Input:
   • Accept image or PDF files.
2. Preprocessing:
   • Enhance image quality if the input is an image.
3. Text Analysis:
   • Extract, clean, and summarize text (using OpenAI and Text Analytics for Health)
4. Mutation Detection:
   • Identify potential mutations with confidence scores.
5. Enrichment:
   • Fetch detailed data for detected mutations from Ensembl.org.
6. Output:
   • Provide structured results for integration with databases or other systems.

Use Cases

• Patient Registries:
  • Populate genetic information for research or clinical databases.
• Research Tools:
  • Provide insights for studies on Rett Syndrome and related conditions.
• Custom Applications:
  • Integrate with applications using flexible workflows and output formats.

Design Highlights

• High Flexibility:
  • Modular design supports various workflows (batch, single-file, triggered).
• Separation of Concerns:
  • Focused on analysis; storage is left to external systems.
• Pydantic Models:
  • Facilitate easy integration with databases like CosmosDB.

Future Enhancements

• Add support for fallback mechanisms to handle errors gracefully.
• Extend functionality to detect mutations in other genes or conditions.
• Implement additional preprocessing for specialized input types (e.g., handwritten documents).
• Enable multilingual text analysis for broader applicability (pending to validate with an extended dataset)