Abstract Challenges preventing mainstream use of RNA-sequencing (RNA-seq) in genome diagnostics are sources of biological and technical variation, typically caused by intrinsic differences in gene expression between tissue types, cellular conditions, and environmental factors. While machine learning methods may partially correct unwanted variation, interpreting RNA-seq data that are typically generated by different sources over time, which is a realistic scenario in healthcare, remains challenging and complex. We developed a complete RNA-guided workflow that handles such variation and is therefore able to identify gene–disease associations in the context of genomic, phenotypic, and segregation analysis of rare disease patients. The result is a streamlined implementation of OUTRIDER and FRASER, complemented with Borzoi and MOLGENIS VIP. This novel workflow paves the way for pinpointing rare variants affecting gene expression and splicing using self-contained interactive reports visualizing outlier genes and prioritized patient-level variants for immediate clinical interpretation. We analysed 144 cases from different centres, a realistic cohort for centres more likely to be dependent on background cohorts. We demonstrate that RNA outlier analysis enhances variant interpretation and, despite its limitations, is already able to aid clinical variant interpretation. Our workflow accelerates the prioritization of coding and non-coding variants, and the reclassification of clinically relevant variants of unknown significance.
Maassen et al. (Tue,) studied this question.