2026/06/01 by Joseph K. Aicher, Dina Issakova, Barry Slaff +8 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #Genetic factors in colorectal cancer #Genomic variations and chromosomal abnormalities #Genomics and Rare Diseases
paper · doi:10.1016/j.gim.2026.102628
openalex publication_date 2026/06/01 · openalex created_date 2026/06/10 · openalex updated_date 2026/07/22
PURPOSE: The current diagnostic rate for patients with suspected Mendelian genetic disorders is low, despite exome/genome sequencing (ES/GS) being the standard of care. One reason for this low diagnostic rate is that traditional ES/GS analysis methods struggle to detect RNA splicing aberrations. Causative variants often involve splicing changes, with numerous splice-altering variants being responsible for known Mendelian disorders. It is therefore crucial to develop reliable tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing. METHODS: We developed MAJIQ-CLIN, a method to identify RNA splicing aberrations in patients' RNA-Sequencing compared to a cohort of control samples. MAJIQ-CLIN can efficiently process large datasets, avoiding reprocessing when new data is added, while effectively detecting local splicing variations (LSV) with deviations in a given patient, termed outlier LSV (oLSV), or unique to the patient, termed private LSV (pLSV). RESULTS: We perform a systematic evaluation of the accuracy of tools for detecting patients' RNA splicing aberrations from RNA-Seq using synthetic data across several aberration types and transcript inclusion levels. We then use several real datasets to assess MAJIQ-CLIN's ability to identify solved test cases and control the effect of confounders such as batches. We show that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency, then use MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network. CONCLUSIONS: MAJIQ-CLIN offers an efficient, accurate, and user-friendly tool to aid in diagnosing Mendelian-causing variants from RNA-Seq data.