Detection of aberrant events in RNA for clinical diagnostics

Abstract: Rare diseases are estimated to affect 3.75% of the global population, which roughly translates to 300 million affected individuals. A large proportion of patients still do not have their diagnosis and current approaches such as chromosomal microarray (CMA), whole exome sequencing (WES), and whole genome sequencing (WGS) that targets DNA and the exome aims to resolve that very first step. RNA-seq serves as a powerful approach complementing the aforementioned methods that have reached a plateau in the diagnostic yield. RNA-seq can facilitate the finding of aberrant events that appear during transcription e.g., splicing, changes in gene expression and monoallelic expression. In this study, we aimed to establish RNA-seq analysis pipelines and evaluate whether RNA-seq could be utilized to enhance diagnostic yield. A total of 47 clinical samples were analysed along with the publicly controlled GEAUVADIS dataset to evaluate the potential of RNA-seq in a clinical setting. The pilot pipeline used, an RNA-seq analysis wrapper around Detection of RNA Outlier Pipeline (DROP), used detected a highly ranked splicing variant in a positive control control  sample that was hard to identify in a WGS analysis. The remaining two other positive control other two control samples with aberrant expression were also detected by the pipeline. Additionally, the pipeline gave a manageable list of candidate genes per affected sample in the population along with corroborating graphs that can support the decision-making for clinicians. The results of this pipeline proved successful for integrating RNA-seq and thustherefore, we expect anticipate an increase in diagnosis.