A scalable solution for NGS variant call sets, clinical reports, and variant assessments​

Introduction

Genomic medicine has advanced significantly with the clinical adoption of DNA exome sequencing (ES) and genome sequencing (GS). Current diagnostic yield varies significantly by method, center and condition, with reported ranges of 19-68% for GS and 7-72% for ES (Nurchis et al., 2023), but the average rate remains low (38.6% and 37.8% respectively)and many cases lack definitive diagnosis. In addition to DNA, next generation technologies have revolutionized RNA sequencing (RNA-seq), enabling high-throughput, massively parallel analysis of transcriptomes, allowing insights into gene expression, structural variants (e.g. alternative splicing, gene fusions (Lappalainen T et al. 2013; Brechtmann et al., 2018), novel isoforms, allele‑specific expression, evidence of nonsense‑mediated decay (Scotti & Swanson, 2016;  Abramowicz & Gos, 2018) and identification of variants messed by DNA sequencing (Li et al 2025)

Whilst DNA is generally considered static and uniform across the cells of an organism and throughout life, RNA is dynamic – changing over time, in response to environment and across cells. RNA-seq captures these dynamic transcriptional consequences that can reveal the functional impact of genomic variants.  

Rationale for Integrated DNA-RNA Analysis

Transcriptomic evidence is increasingly recognized as essential in neuromuscular, mitochondrial, and metabolic disorders (Murdoch et al., 2021; Gonorazky et al 2019; Cummings et al., 2017 ). RNA‑seq provides critical evidence in rare disease diagnostics, particularly for disorders involving splicing, regulatory variants, and monoallelic expression (Frésard et al., 2019). Importantly, integrating both DNA and RNA has been shown to improve diagnostic yield both in rare disease and oncology (Horton et al., 2024; Peymani et al., 2022)

RNA-seq to resolve VUS

Increasingly, RNA-seq is used as a functional validation tool for reclassifying variants of uncertain significance (VUS) (Ellingford et al., 2022; Vaz-Drago et al., 2017). It has been predicted that approximately one-third of splicing VUSs could reach either a likely pathogenic or likely benign classification from RNA-seq analysis (Truty et al., 2021). One study assessed 21 unique VUS (16 intronic, 2 synonymous, 3 missense) via RNA-seq analysis, with confirmation or rebuttal of a transcript effect in all 21 variants. Exon skipping, exon extension, intron retention and nonsense medicated decay were identified (Stark et al., 2025).

RNA-seq to resolve canonical splice site variants and atypical phenotypes

As part of a study investigating four specific clinical scenarios and the diagnostic utility of RNA-seq, Stark et al (2025) investigated canonical splice variants in known disease genes in probands presenting with an atypical phenotype (n=3).  Whilst the variants in known disease genes were classified as pathogenic, the atypical phenotype of the patient made the finding uncertain for the cause of disease. In all cases RNA-seq confirmed impact on splicing and provided evidence for the unique phenotype.

RNA-seq to resolve impact of intragenic copy number variants (CNVs)

RNA-seq can determine the impact of intragenic CNVs, for example in a case of central hypotonia and infantile spasms, a de novo intragenic tandem duplication was observed via WGS (Xp22, 13p22.13(18474185_18579246), which included the first coding exon of CDKL5, the gene associated with CDKL5 deficiency disorder. Whilst the DNA variant was deemed pathogenic, due to the patient’s atypical presentation further investigation was undertaken, with RNA-seq confirming the duplication was in tandem, with splicing resulting in an in-frame transcript and only some level of nonsense mediated decay (NMD), which was likely an explanation for the probands mild phenotype (Stark et al., 2025).

RNA-seq to identify candidate genes

RNA-seq can be used to resolve diagnosis by detecting aberrant splicing events in patients with no candidate DNA sequence variants (4/32; Jaramillo Oquendo et al., 2024). Pathogenic mechanisms missed by DNA‑only approaches can include expression outliers and deep‑intronic splice‑altering variants (11,12).

RNA-seq to resolve variants in UTR

DNA variants in untranslated regions (UTR) of genes consistent with patient phenotype, have been shown via RNA-seq to result in altered transcript expression, allele bias and possible gene fusion events (Stark et al., 2025).

Integrated multi‑omics improves diagnostic outcomes across multiple clinical settings (Lunke et al., 2023; Stark et al., 2019). Adding RNA to the molecular testing repertoire, increases diagnostic yield by 8-36%, depending on the cohort (Yepez et al., 2022; Lunke et al., 2023; Gonorazky et al., 2019; Kremer er al., 2017; Frésard et al., 2019). In up to 18 percent of resolved cases, RNA evidence directly enabled diagnosis (Lunke et al., 2023).

Implementation

Whilst the challenges of RNA-seq have slowed clinical adoption, including accessibility to relevant tissue, instability of RNA in laboratory processes and lack of analytical tools to manage and interpret the large amount of data generated, these are being overcome and RNA-seq is now a feasible option in the clinical laboratory tool kit.

Integrated multi‑omics requires robust bioinformatics infrastructure. Combined analytical systems must harmonize reference genomes, transcript annotations, variant files, expression matrices, splice junction data, and fusion calls (Morales et al., 2022).

Conclusion

Integrated DNA-RNA analysis represents a major advancement in clinical genomics, aiding variant interpretation, improving diagnostic yield and clinical decision‑making As sequencing technologies and computational frameworks evolve, multi‑omics approaches will become essential in routine diagnostics, with high diagnostic utility as an ancillary test (Stark et al., 2025).

Geneyx and OmicsDiscoveries

The collaboration between Geneyx and OmicsDiscoveries combines Geneyx’s multi-omics interpretation system with OmicsDiscoveries’ expertise in transcriptomics and functional data generation, paving the way for deeper biological insights and improved understanding of disease mechanisms. Together we aim to bridge the gap between genomic variation and biological function, empowering researchers and clinicians with more comprehensive tools for discovery and diagnosis.

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