From Genomic Diagnosis to Personalized RNA Medicine: Advances in Next-Generation Sequencing and N-of-1 Antisense Oligonucleotide Therapies for Rare Genetic Diseases
Bibliographic record
Abstract
Next-generation sequencing (NGS) and antisense oligonucleotide (ASO) technologies are converging to transform the diagnosis and treatment of rare monogenic disorders. NGS enables comprehensive, single-test molecular diagnoses through targeted panels, whole-exome (WES), and whole-genome sequencing (WGS), which together reveal pathogenic variants across coding, intronic, and structural domains. Integration with transcriptomic analyses, including RNA sequencing, further refines genotype–phenotype correlations and identifies splicing aberrations amenable to correction by ASOs. Therapeutic advances now span RNase H-dependent gapmers for transcript knockdown, splice-modulating phosphorodiamidate morpholino oligomers (PMOs), and peptide/antibody-conjugated PMOs that enhance muscle and cardiac delivery. These platforms underpin the rise of N-of-1 ASO therapies—customized drugs developed for individual patients with unique pathogenic variants. Landmark cases such as Milasen, Valeriasen, and Atipeksen illustrate the clinical feasibility and ethical complexities of personalized RNA therapeutics, while updated FDA guidance supports expedited, patient-specific investigational pathways. Despite progress, challenges persist in delivery efficiency, long-term efficacy, and equitable access. Emerging approaches—including long-read sequencing, AI-driven oligo design, and improved nanoparticle delivery—promise to extend ASO precision and reach. This review synthesizes current advances linking genomic diagnosis to individualized RNA-targeted interventions, outlining how integrated NGS-ASO pipelines are reshaping the therapeutic landscape for rare genetic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".