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

Traditional genetic research has predominantly focused on the approximately 2% of the genome that encodes proteins—those molecular workhorses indispensable for myriad biological functions. Yet, extensive genome-wide association studies (GWAS) have unearthed compelling evidence that variants lurking outside these coding sequences wield significant influence over health and disease. These noncoding variants often operate within regulatory domains, orchestrating when and how genes are expressed through the modulation of DNA-protein interactions. However, deciphering this “regulatory code” has proved profoundly challenging due to the complex interplay of transcription factors—the proteins that bind specific DNA sequences to control gene activity—and the subtle nature of their genomic footprints.

The pioneering study addresses a critical bottleneck in genetic analysis: distinguishing causative variants from a constellation of nearby candidates within noncoding loci associated with disease. Because many of these variants cluster around transcription factor binding motifs, the precise delineation of where these proteins latch onto the genome can illuminate which variant actually disrupts gene regulation. The research hinges on a nuanced understanding of the transcription factor “footprint,” a term denoting the localized suppression of DNA accessibility at binding sites following protein attachment. This footprint acts like a molecular signature, detectable through sophisticated sequencing techniques, that reveals the exact coordinates where transcription factors exert their influence.

To capture this elusive footprint, the researchers employed ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing), a powerful experimental technique that maps open, accessible regions of the genome amenable to protein binding. By coupling ATAC-seq data from 170 distinct human liver tissue samples with PRINT, a novel deep learning framework designed to discern subtle DNA-protein interactions, the team generated an unprecedented high-resolution map of transcription factor footprints. These maps enabled the identification of “footprint quantitative trait loci” (fpQTLs)—specific genomic sites where variation in DNA sequence correlates with altered footprint patterns, implicating differential transcription factor binding attributable to genetic variants.

This integrative methodology represents a quantum leap in resolving the “needle in the haystack” problem that has bedeviled geneticists. By refining the localization of functional regulatory variants within broad disease-associated regions, scientists can now approach the heretofore nebulous noncoding genome with surgical precision. Beyond liver tissue, the researchers envision extending this approach to a variety of organs and cellular contexts, which could vastly accelerate the identification of disease-driving variants across diverse conditions including metabolic disorders, psychiatric illnesses, and beyond.

Senior author Dr. Struan F.A. Grant eloquently analogized the challenge, likening it to a police lineup where multiple suspects—genetic variants—appear similar, yet only one bears culpability. Through the ability to map precise transcription factor footprints influenced by DNA sequence changes, the team has effectively enhanced the investigative toolkit, enabling confident identification of the “culprit” variants most likely to contribute to pathogenesis. This capability promises to fill a critical knowledge gap in the translation of GWAS findings into actionable biological insight.

The research initiative was made possible in part by the multidisciplinary integration of computational modeling, high-throughput experimental genomics, and biostatistical rigor. PRINT, the deep learning algorithm central to footprint detection, exemplifies the transformative power of artificial intelligence in genomics, as it can parse complex and noisy biological data far beyond human capacity. The application of such computational sophistication to ATAC-seq datasets has dissected the nuanced interplay between nucleotide variation and transcription factor binding strength, uncovering patterns invisible to conventional analyses.

Furthermore, the study’s focus on liver samples holds particular pertinence given the organ’s central role in metabolism, detoxification, and disease susceptibility. By characterizing liver-specific fpQTLs, the team has laid a vital foundation for understanding how regulatory variants may influence conditions such as metabolic syndrome, liver fibrosis, and other prevalent disorders. Moreover, the principles delineated here are broadly transferable, as regulatory mechanisms mediated by transcription factors are a universal feature of cellular function.

First author Max Dudek highlighted the profound implications of these findings for precision medicine. The capacity to accurately pinpoint noncoding variants that actively modulate gene expression shifts the paradigm from correlation to causation in genetic risk assessment. With ongoing expansion to larger cohorts and diverse tissue types, this approach might ultimately enable bespoke intervention strategies, wherein patients are treated based on the precise regulatory variants driving their disease—a leap towards truly personalized therapeutics.

In addition to its clinical potential, this research also advances fundamental biological understanding. Noncoding DNA has traditionally been understudied relative to coding counterparts, yet it harbors myriad regulatory elements governing cellular identity and responsiveness. The fine-scale mapping of transcription factor footprints offers a window into the dynamic regulatory architecture of the genome, elucidating how genetic variation sculpts the gene expression landscape underpinning health and disease.

Funding for the investigation was provided by prestigious institutions including the National Science Foundation Graduate Research Fellowship Program and multiple National Institutes of Health grants. The confluence of public investment and academic ingenuity underscores the societal value attributed to decoding the regulatory genome, which stands as a frontier of modern biomedical science.

Looking ahead, the researchers aspire to augment their footprint QTL atlas with integrative multi-omics data, including chromatin conformation, epigenetic modifications, and transcriptomics, to construct a holistic model of gene regulation perturbed by noncoding variants. Such composite frameworks could dramatically enhance predictive modeling of disease risk and responsiveness to therapy.

This seminal work, published in the American Journal of Human Genetics on April 17, 2025, marks a watershed moment in genomics research. By illuminating the shadows of the noncoding genome, the study opens new avenues for discovery and innovation, promising to transform our approach to diagnosing, preventing, and treating a kaleidoscope of human diseases through the lens of genetic regulation.

Subject of Research: Human tissue samples
Article Title: Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver
News Publication Date: 17-Apr-2025
Web References: Children’s Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, American Journal of Human Genetics
References: Dudek et al, “Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver.” Am J Hum Genet. Online April 17, 2025. DOI: 10.1016/j.ajhg.2025.03.019.
Keywords: Genetic variation, Psychiatric disorders, Discovery research, Basic research