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Updated: Mar 12, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
HiFi long-read RNA sequencing enhances clinical diagnostics in rare disorders
Carolina Jaramillo Oquendo1, Federico Ferraro2, Htoo A Wai1
1Human Genetics and Genomic Medicine, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
Abstract:
Splice-disrupting variants are estimated to account for one-third of disease-causing variants, yet many remain underrepresented in clinical databases due to limitations in detecting splicing changes beyond canonical splice sites. Short-read RNA sequencing (RNA-seq) has proved to be a valuable complement in clinical practice to address this gap, however, the added value of long-read RNA-seq is unclear. We evaluated the potential of PacBio long-read RNA-seq to detect pathogenic splicing events in rare disorders, comparing its performance to short-read RNA-seq. Participants from the UK (n = 23) and the Netherlands (n = 2) with suspected splice-altering variants underwent long-read RNA-seq following the Kinnex full-length RNA protocol. HiFi reads from the Revio instrument were processed using the Read Segmentation and Iso-Seq workflow and then classified and filtered using Pigeon. Detection of disease genes was comparable with short reads, with fibroblast capturing more transcripts overall. Novel isoforms accounted for ~14% of detected transcripts in both tissues, increasing following cycloheximide treatment in fibroblasts and decreasing following globin depletion in blood. Transcript abundance estimates showed strong concordance between short- and long-read platforms (Pearson r = 0.86 and 0.61 in blood and fibroblasts, respectively). LRS captured 21 confirmed known events, and revealed additional transcript-level effects in eight cases. This included intron retention, multiple exon skipping, leaky splicing, variant phasing, and isoform switching. These results demonstrate that long-read RNA-seq enhances detection and interpretation of clinically relevant splicing events, supporting its integration into diagnostic workflows for rare diseases.
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