Mutadelic: mutation analysis using description logic inferencing capabilities.

Area of Science:

• Genomics
• Bioinformatics
• Clinical Genetics

Background:

• Next-generation sequencing generates vast amounts of patient variant data, necessitating efficient annotation tools for clinical genetics.
• Existing bioinformatics tools often produce excessive information, hindering scalability in variant data analysis.
• Characterizing patient variants is crucial for identifying clinically relevant mutations.

Purpose of the Study:

• To develop an alternative annotation solution using description logic inferencing for generating targeted variant annotation workflows.
• To create a system that produces only relevant annotations for variant interpretation, enhancing scalability.
• To implement criteria for identifying disease-causing variants in Mendelian blood disorders.

Main Methods:

• Utilized a novel abductive reasoning framework, the basic framework for abductive workflow generation (AbFab), for dynamic workflow generation.
• Implemented criteria for identifying disease-causing variants in Mendelian blood disorders as AbFab services.
• Developed a web application (Mutadelic) for users to run generated workflows and analyze genomic variants.

Main Results:

• Dynamically generated workflows produced only annotations contributing to variant interpretation.
• AbFab services successfully identified and flagged significant variants in Mendelian blood disorders.
• The Mutadelic web application provided explanations for variant classification based on implemented criteria.

Conclusions:

• AbFab offers a scalable and efficient approach to variant annotation by generating targeted workflows.
• The developed system aids in the interpretation of genomic variants for clinical applications.
• This method improves the identification of disease-causing mutations in Mendelian blood disorders.