MAJIQ-CLIN: A novel tool to help identify Mendelian disease-causing variants from RNA-seq data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• The diagnostic rate for Mendelian genetic disorders remains low, even with exome/genome sequencing (ES/GS).
• Traditional ES/GS methods often fail to detect RNA splicing aberrations, which are implicated in many genetic disorders.
• Developing tools to analyze RNA splicing variations is critical for improving genetic diagnoses.

Purpose of the Study:

• To develop and evaluate MAJIQ-CLIN, a computational tool for identifying RNA splicing aberrations in patient RNA sequencing data.
• To improve the detection, quantification, prioritization, and visualization of splice-altering variants.
• To enhance the diagnostic yield for Mendelian genetic disorders.

Main Methods:

• MAJIQ-CLIN was developed to compare patient RNA sequencing data against control cohorts.
• The method efficiently processes large datasets and identifies local splicing variations (LSVs) as outlier (oLSV) or private (pLSV) events.
• Systematic evaluation used synthetic and real-world datasets, including solved cases and analysis of unsolved patient cases from the Undiagnosed Diseases Network.

Main Results:

• MAJIQ-CLIN demonstrated high accuracy in detecting various RNA splicing aberrations across different transcript inclusion levels.
• The tool showed favorable performance compared to existing methods in both accuracy and efficiency.
• MAJIQ-CLIN successfully identified splice-altering variants in unsolved patient cases.

Conclusions:

• MAJIQ-CLIN provides an efficient and accurate solution for analyzing RNA splicing aberrations.
• The tool aids in the diagnosis of Mendelian genetic disorders by identifying causative variants from RNA-Seq data.
• MAJIQ-CLIN is a user-friendly resource for clinical geneticists and researchers.