New Long-Read RNA Sequencing Workflow Cracks Tough Splicing Variants in Rare Disease

Researchers in the United Kingdom have developed a streamlined laboratory and analysis workflow, called RAPID, that brings targeted long-read RNA sequencing out of the research setting and into the diagnostic arena for rare genetic disease. Writing in the journal Genome Medicine, the team, led by Kylie-ann Montgomery and Mina Ryten of University College London and collaborators at multiple NHS and academic centres, reports that the approach delivered actionable findings for all six unsolved neurometabolic cases tested, demonstrating that near-full-length transcript reading can provide the functional evidence needed to settle variants that short-read sequencing cannot.

The clinical problem the team set out to address is well known in genomic medicine. Molecular diagnosis of rare disease currently plateaus at roughly fifty percent of patients, even after whole exome or whole genome sequencing. A substantial share of those unresolved cases involves variants that alter splicing, the process by which RNA transcripts are cut and re-joined to produce mature messenger RNA. Predicting whether a DNA change actually disrupts splicing in a patient’s tissues remains notoriously difficult, and variants of uncertain significance accumulate in reports without a practical way to test their effects directly.

Short-read RNA sequencing, which fragments transcripts into small pieces before reading them, often cannot show how exons connect across a whole transcript. Long-read platforms from Oxford Nanopore Technologies solve this by reading RNA-derived molecules end to end, revealing full isoform structures. Until now, however, long-read RNA approaches have typically demanded large control cohorts, complex bioinformatics and tissue samples that are hard to obtain, keeping them confined to specialist research laboratories rather than routine diagnostics.

RAPID, short for RNA Analysis Pipeline for Integrated Diagnostics, was designed from the outset to be diagnostically deployable. It is fully modular, covering the entire sample-to-answer journey, from primer design targeting genes of interest through nanopore sequencing to reproducible single-sample interpretation. Cases entered the workflow after exome or genome sequencing and multidisciplinary team review had narrowed the search to five or fewer candidate genes, meaning the sequencing effort could be focused and fast. Crucially, the method relies on accessible tissues such as blood and does not require large control datasets for interpretation.

The study applied the workflow to six probands with suspected monogenic neurometabolic disease, and the outcomes illustrate three distinct diagnostic scenarios. In two cases, targeted long-read RNA sequencing confirmed pathogenic splice disruption at the transcript level, converting uncertainty into a resolved molecular diagnosis. In one case, the RNA evidence argued against a candidate gene, prompting its exclusion and redirecting the diagnostic search. In the remaining three cases, transcript-level findings prioritised further DNA investigation, refining how the variants of uncertain significance should be weighed.

Technically, the workflow captured amplicons spanning the relevant exons and splice junctions of each candidate gene, generating reads long enough to assemble near-full-length isoform structures. The team demonstrated reproducibility by showing consistent isoform composition for a control gene across commercial blood RNA samples and an independent public long-read dataset, and they characterised the minimum read depth needed to detect transcripts at low fractional abundance. Quality control metrics, including read length and depth, were achieved using standard long-read sequencing infrastructure within a clinically relevant timeframe.

The authors also mapped their RNA results onto the established variant interpretation framework used by clinical scientists, in which aberrant splicing leading to frameshifts or premature stop codons, particularly in transcripts subject to nonsense-mediated decay, can support pathogenic classifications. By providing direct, mechanism-level evidence rather than in silico predictions, the workflow strengthens this evidence hierarchy and gives diagnostic laboratories a practical route to resolving variants that would otherwise remain reportable only as uncertain.

The significance for patients and families is considerable. A definitive molecular diagnosis can end a diagnostic odyssey that sometimes lasts decades, inform prognosis, guide surveillance and treatment, enable accurate genetic counselling, and open doors to targeted therapies and clinical trials. For a field in which half of patients still leave the sequencing process without answers, a rapid, cost-effective test that interrogates RNA directly addresses one of the largest remaining diagnostic gaps.

The researchers argue that RAPID shows long-read RNA sequencing can be implemented within existing diagnostic infrastructure, offering a scalable path to routine transcript-level assessment in clinical genomics. As nanopore sequencing becomes more widespread in NHS and hospital laboratories, workflows of this kind could shift splicing variant interpretation from probabilistic prediction to direct functional measurement. Supported by funding from the charity Sparks and conducted with appropriate ethical approvals and participant consent, the study points toward a future in which reading the transcript itself becomes a standard step in solving rare disease.

The biological importance of splicing in human disease provides useful context for why this approach matters. Introns interrupt most human genes, and their precise removal depends on short sequence signals at exon boundaries that are frequently disrupted by single-nucleotide changes lying well outside protein-coding regions. Because such variants often sit in positions that protein-prediction tools ignore, they can be classified as benign or left as uncertain even when they silently abolish a transcript. Deep intronic changes can also activate cryptic exon inclusion, an effect essentially invisible to standard exome analysis, which is one reason splice-altering variation has been described as a substantial hidden burden within the undiagnosed fraction of rare disease cohorts.

The interpretation framework applied in the study reflects a broader international movement toward using RNA evidence in clinical classification. Guidelines from professional bodies now allow aberrant splicing demonstrated at the transcript level to contribute to pathogenic classifications, provided the altered transcript is shown to escape nonsense-mediated decay or to produce a clearly deleterious product. This matters because a large proportion of loss-of-function variants predicted to trigger nonsense-mediated decay are already treated as pathogenic by default; demonstrating experimentally that a variant of uncertain significance produces the same consequence effectively moves it into that well-established category. Conversely, showing that a candidate variant leaves splicing intact can be equally decisive, as the gene-exclusion case in this cohort illustrates.

Tissue choice is a central consideration for any transcript-based diagnostic test. Genes are not expressed uniformly across the body, and the reference resources that underpin transcript interpretation, such as population-scale tissue atlases, show that many disease-relevant genes have their highest expression in tissues that cannot ethically or practically be sampled. Blood, however, is accessible, and the workflow’s reliance on peripheral blood RNA, collected in standard preservation tubes and stored frozen, means the logistics resemble those of routine clinical phlebotomy rather than specialist tissue procurement. The supplementary analyses showing that isoform composition of a control gene is consistent across commercial RNA references and public datasets speak to the reproducibility that regulators and accreditation bodies would expect of a deployable assay.

The depth requirements characterised by the team address a practical question that any diagnostic laboratory must answer before adopting such a test: how much sequencing is enough. Because a deleterious transcript may represent only a small fraction of all transcripts from a given gene, particularly when nonsense-mediated decay degrades the abnormal product rapidly, sensitivity at low fractional abundance is essential. Mapping the relationship between transcript rarity and required read depth gives laboratories a principled basis for setting sequencing targets and quality thresholds rather than relying on ad hoc criteria.

The study also sits within a distinctive UK genomic infrastructure. Several participating families had previously been sequenced through national genome programmes, and the ethical frameworks governing those programmes permit recontact of participants for follow-up sampling, a mechanism that allowed the researchers to obtain fresh RNA from already-investigated patients. This recontact pathway, coordinated through clinical interpretation partnerships, represents a model for how long-read RNA follow-up could be layered onto existing genomic medicine services without requiring patients to restart the consent and recruitment process from scratch.

From a health-systems perspective, the economics of the approach deserve emphasis. Whole genome sequencing has become affordable at scale, but the interpretation bottleneck, not the sequencing itself, now limits diagnosis. A targeted assay that sequences only a handful of genes consumes modest sequencing capacity on instruments that many hospital laboratories already operate or can readily access, and the modular design means primer sets can be redesigned quickly for each new case. The near-full-length isoform output also reduces interpretive ambiguity, because the analyst sees the complete exon connectivity of each transcript rather than inferring it from fragmented short reads.

The neurometabolic focus of the cohort is itself informative. Leukodystrophies and related neurodegenerative conditions of childhood often present with nonspecific imaging findings and progressive symptoms, and several of the genes implicated in these disorders are known to harbour splice-disrupting variants that confound standard pipelines. For families facing such progressive conditions, the speed of the workflow is not merely convenient; an earlier molecular answer can determine eligibility for emerging therapies, some of which are most effective when started before irreversible neurological damage accumulates.

Looking forward, the study suggests a tiered model of genomic diagnosis in which short-read sequencing remains the first-line discovery tool, while targeted long-read RNA analysis serves as a rapid functional triage step for the subset of cases with splice-relevant candidates. The authors’ demonstration that results can be obtained within a clinically relevant timeframe, using standard infrastructure and single-sample interpretation without large control cohorts, positions transcript-level evidence to become a routine component of multidisciplinary review rather than a research exception reserved for specially funded projects.

Subject of Research: A targeted long-read RNA sequencing workflow for functionally resolving splicing variants in rare disease diagnosis

Article Title: RAPID: a targeted long-read RNA workflow for functional resolution of splicing variants in rare disease

Article References: Montgomery, K.-A., Macpherson, H., Anderson, C., Wade, C., Gustavsson, E. K., Lynch, D. S., Wilson, L. C., Davison, J., Wakeling, E., Tuschl, K., Houlden, H., Clement, E., Mills, P. B., & Ryten, M. (2026). RAPID: a targeted long-read RNA workflow for functional resolution of splicing variants in rare disease. Genome Medicine. https://doi.org/10.1186/s13073-026-01754-3

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01754-3

Keywords: long-read RNA sequencing, Oxford Nanopore Technologies, splicing variants, rare disease diagnostics, variants of uncertain significance, neurometabolic disease, transcriptomics, molecular diagnosis, clinical genomics, whole exome sequencing, RNA splicing, diagnostic workflow