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

Traditional genetic diagnostics often rely heavily on exome and whole-genome sequencing to identify mutations responsible for rare diseases. However, such methods, while powerful, exhibit a diagnostic yield ranging only between 20% to 50%, leaving a majority of patients without concrete molecular diagnoses. This diagnostic gap emerges largely because DNA sequencing alone cannot capture the complexities of RNA transcription and processing alterations caused by certain genetic variants. To bridge this critical knowledge gap, scientists have increasingly turned towards RNA sequencing, which directly interrogates the molecules that convey genetic information into proteins, thus providing a clearer functional context.

The CHOP team, led by Dr. Yi Xing, Associate Chief Scientific Officer for Omics, Technology & Engineering, and a luminary in computational and genomic medicine, addressed these challenges by developing STRIPE—short for Sequencing Targeted RNAs Identifies Pathogenic Events. STRIPE leverages the advantages of long-read RNA sequencing technology, which differs from traditional short-read RNA sequencing by reading entire RNA molecules in one continuous stretch. This full-length sequencing capability is crucial as it preserves the context of multiple genetic variants and splicing events across the same RNA transcript, enabling a high-resolution view of the molecular consequences of genetic mutations.

Despite the immense potential of long-read RNA sequencing, its adoption in clinical diagnostics has been hindered by issues including high costs, lower throughput, and suboptimal accuracy compared to short-read methods. The STRIPE platform surmounts these obstacles by integrating targeted sequencing with a cost-effective, scalable workflow built upon CHOP’s predecessor technology, TEQUILA-seq. TEQUILA-seq was initially developed to offer a versatile and affordable means of sequencing entire RNA molecules, facilitating large-scale studies without prohibitive expense. STRIPE advances this concept further, focusing sequencing depth on bespoke panels of disease-relevant genes, enabling ultra-deep sequencing at an approximate RNA-to-data cost of $100 per sample—an affordability milestone that makes routine clinical application feasible.

The efficacy of STRIPE was rigorously evaluated using cohorts of individuals affected by congenital disorders of glycosylation (CDG) and primary mitochondrial diseases (PMD), two groups of genetically heterogeneous rare diseases extensively researched at CHOP. These diseases are characterized by complex pathogenic mechanisms and subtle RNA perturbations, making them ideal candidates to validate the diagnostic precision and mechanistic insights provided by STRIPE. Notably, the study demonstrated that STRIPE could accurately detect previously known pathogenic variants, elucidate the intricate RNA processing consequences of these mutations, and crucially, identify novel disease-causing variants in patients who had previously remained without diagnosis despite exhaustive testing.

A pivotal advantage of the STRIPE strategy is its ability to analyze RNA extracted from clinically accessible tissues such as patient blood or skin fibroblasts. This feature addresses a longstanding challenge in RNA-guided diagnostics: the difficulty in obtaining disease-relevant tissue samples from patients due to invasiveness or inaccessibility. Dr. Rebecca Ganetzky, a clinical geneticist at CHOP’s Mitochondrial Medicine Program, emphasized that STRIPE’s capacity to derive meaningful diagnostic signals from such accessible samples profoundly enhances its clinical utility, enabling physicians to interrogate RNA-level disruptions without invasive biopsies.

This innovative platform extends beyond mere diagnostic yield. By providing a detailed map of how specific genetic variants alter RNA molecules—through mechanisms such as aberrant splicing, transcript truncation, and expression imbalances—STRIPE bridges the longstanding divide between genetic findings and functional understanding. This molecular granularity equips clinicians with actionable insights into disease mechanisms, facilitating informed clinical decision-making and opening pathways towards tailored therapeutic interventions targeted at the underlying RNA dysfunction.

Collaborative efforts with specialized clinical programs, such as CHOP’s CDG Clinic directed by Dr. Andrew C. Edmondson, validated STRIPE’s diagnoses by correlating them with measurable biochemical disruptions in glycosylation pathways. This multidisciplinary integration not only verified the technology’s clinical relevance but also accelerated patient access to molecular diagnoses that had previously been unattainable by conventional methods. These achievements underscore the potential of STRIPE to conclude often-protracted diagnostic odysseys for patients afflicted with rare diseases, thereby improving their clinical management and quality of life.

The comprehensive evaluation encompassed 88 individuals, including rare disease patients and healthy controls, and unequivocally demonstrated STRIPE’s sensitivity and specificity in detecting pathogenic RNA alterations. Beyond confirming known variants, the platform revealed complex RNA consequences of mutations that had been underestimated, thereby refining the interpretation of variants of uncertain significance—a notorious obstacle in clinical genetics. Perhaps most strikingly, STRIPE yielded new molecular diagnoses in five patients previously undiagnosed after exhaustive genetic work-ups, emblematic of its transformative clinical potential.

Since its inception, STRIPE has been deployed on over 500 patients within various CHOP clinical programs, reinforcing its robustness, scalability, and readiness for integration into real-world rare disease diagnostic pipelines. This extensive application highlights the platform’s adaptability to diverse clinical contexts and its promise to catalyze advances in personalized medicine through RNA-guided precision diagnostics.

The scientific implications of STRIPE extend into the therapeutic realm. By elucidating the RNA-level disruptions caused by genetic variants, this methodology paves the way for the development of RNA-targeted therapies—ranging from antisense oligonucleotides to RNA editing strategies—that directly rectify pathogenic transcripts. This convergence of diagnostic precision and therapy holds the promise of ushering in a new era of precision medicine in rare diseases, where molecular diagnoses seamlessly inform bespoke treatment strategies.

Fundamentally, STRIPE embodies a paradigm shift in rare disease genomics, moving beyond DNA-centric views to embrace the transcriptional and post-transcriptional complexities that drive disease. As Dr. Yi Xing articulated, the platform represents “a bridge from genetic diagnosis to disease mechanism to targeted therapies,” heralding a future where full-length RNA sequencing is a cornerstone of clinical genetics.

This cutting-edge research was supported by a multitude of grants from the National Institutes of Health and institutional programs at CHOP, emphasizing the collaborative and well-funded nature of this endeavor. Moreover, the CHOP team has filed a patent application for the STRIPE technology, signifying its potential for widespread clinical deployment and commercial translation.

In summary, the development of STRIPE advances a vital frontier in genetics by illuminating the elusive impact of variants at the RNA level with unprecedented clarity, accuracy, and clinical applicability. This innovation marks a critical step toward resolving the diagnostic challenges faced by many rare disease patients worldwide and underscores the transformative power of integrating advanced sequencing technologies into precision medicine frameworks.

Subject of Research: People

Article Title: Targeted long-read RNA sequencing for rare disease diagnosis and variant interpretation

News Publication Date: 15-Apr-2026

Web References: http://dx.doi.org/10.1126/sciadv.ady9895

References: Wang et al, “Targeted long-read RNA sequencing for rare disease diagnosis and variant interpretation.” Sci Adv. Online April 15, 2026. DOI: 10.1126/sciadv.ady9895.

Keywords

Genetics, Pediatrics, RNA sequencing, Long-read sequencing, Rare disease diagnostics, Congenital disorders of glycosylation, Primary mitochondrial diseases, Molecular diagnosis, Precision medicine, RNA-level variant interpretation, CHOP, STRIPE technology

Historically, diagnosing rare diseases has posed significant hurdles. With thousands of variations in human DNA, pinpointing the one responsible for a condition can be likened to searching for a needle in a haystack. As genomic databases swell with genetic information—including variants of uncertain significance—the traditional one-size-fits-all methodology for interpreting these variants can no longer suffice. Variants must be classified carefully, considering not only their individual characteristics but also the overall context of the patient’s phenotype. The methodology introduced by Cooperstein et al. takes critical steps towards addressing these challenges by optimizing how variants are prioritized for further investigation.

At the core of their study are two powerful bioinformatics tools: Exomiser and Genomiser. Exomiser operates by analyzing genomic data in conjunction with specific phenotype information, searching for potential genetic variants that align with a patient’s clinical presentation. Conversely, Genomiser emphasizes the integration of gene-phenotype associations and can be particularly useful in narrowing down candidate genes, especially when the phenotype is not clearly specified. The authors argue that although both tools are invaluable, their full potential is unlocked only when used in a complementary manner, allowing for a more comprehensive analysis of genetic variants.

One of the significant advancements presented in the study is the proposal of a structured prioritization framework that meticulously evaluates variants based on multiple criteria. This multifaceted approach factors in variant rarity, pathogenicity predictions, and the strength of gene-phenotype associations. This systematic evaluation not only streamlines the diagnostic process but also ensures that variants with a higher potential for being disease-causing are identified more efficiently.

Moreover, the researchers advocate for implementing a standardized workflow that integrates these tools within clinical settings. With clear recommendations laid out, the study emphasizes the importance of adopting a structured protocol—ensuring that clinicians are equipped to utilize the capabilities of Exomiser and Genomiser effectively. This recommendation is particularly crucial in pediatric cases, where timely diagnosis can significantly alter treatment outcomes and improve quality of life.

Cooperstein and his team also stress the necessity of collaboration among various stakeholders—including geneticists, bioinformaticians, and clinicians—to enhance the overall effectiveness of rare disease diagnostics. In this connected ecosystem, sharing insights and findings from variant analyses can lead to a more profound understanding of genetic conditions, thus fostering an environment ripe for innovation. This collaborative spirit aims to unify efforts across the scientific community, breaking down silos that often restrict the flow of vital genetic information.

Another layer of complexity that the study addresses is the ethical considerations surrounding genomic data. The authors highlight the importance of informed consent, particularly in the context of using genetic data from individuals who may not fully comprehend the implications of their genomic information. Ethical governance must be integrated into any discussion of variant prioritization processes, ensuring that patient autonomy and privacy remain paramount as genomic sequencing becomes more commonplace.

The practical implications of the study also extend to improving patient management. With an optimized variant prioritization process, clinicians can offer more personalized approaches to treatment, aligning therapeutic interventions with the underlying genetic causes of rare diseases. This paradigm shift can enhance patient outcomes and inform future therapeutic development, as more precise genetic insights allow for targeted therapy modalities.

Beyond the immediate clinical applications, the research by Cooperstein et al. has far-reaching implications for the broader landscape of genomic research. As new genetic variants are continuously identified, the iterative nature of the proposed prioritization framework can accommodate the evolving genomic landscape. This adaptability is crucial in a field characterized by rapid advancements, ensuring that diagnostic processes remain relevant and effective amid constant change.

In conclusion, the optimized variant prioritization process outlined by Cooperstein and his colleagues marks a significant leap forward in rare disease diagnostics. It embodies a critical step toward ensuring that every patient’s unique genetic makeup is considered comprehensively in the diagnostic journey. With tools like Exomiser and Genomiser, and a collaborative, ethical framework guiding their application, the potential for accurately diagnosing rare genetic conditions becomes increasingly attainable. This new paradigm not only enhances the efficiency of genetic testing but also holds the promise of transforming the future of precision medicine.

As we stand on the precipice of a genomic revolution, the insights gleaned from this study serve as a clarion call for clinicians, researchers, and policymakers alike. Together, they can create a more informed and integrated approach to genetics that prioritizes both innovation and ethical responsibility. In a world where genetic information can unlock the mysteries of health and disease, it is imperative that we leverage these advancements to understand and meet the needs of every patient navigating the complexities of rare diseases.

Subject of Research: Optimized variant prioritization in rare disease diagnostics

Article Title: An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser

Article References:
Cooperstein, I.B., Marwaha, S., Ward, A. et al. An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser.
Genome Med 17, 127 (2025). https://doi.org/10.1186/s13073-025-01546-1

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s13073-025-01546-1

Keywords: genomics, rare diseases, variant prioritization, Exomiser, Genomiser, genetic diagnostics, bioinformatics, precision medicine.