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.