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. 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). 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. MAJIQ-CLIN offers an efficient, accurate, and user-friendly tool to aid in diagnosing Mendelian-causing variants from RNA-Seq data.
Aicher et al. (Tue,) studied this question.