OmicsDiscoveries provides an analysis platform that integrates RNA-seq and clinical data to prioritize genetic variants impacting gene expression. Their statistical methods transform variants of uncertain significance into actionable findings by measuring variant impact directly at the RNA level. This workflow improves diagnostic yield for rare disease patients by uncovering previously overlooked pathogenic variants.
Funding
Funding not disclosed
Founders
Product
Problem
Patients with rare genetic disorders often undergo whole‑exome or whole‑genome sequencing without receiving a definitive diagnosis because many identified variants are classified as variants of uncertain significance (VUS) and lack functional evidence of impact on gene expression.
Solution
OmicsDiscoveries offers the RNAvision analysis platform, which combines RNA‑sequencing data with clinical phenotype information to directly assess the transcriptional consequences of genetic variants. The platform applies proprietary statistical tools—OUTRIDER for aberrant gene‑expression detection, FRASER for aberrant splicing and intron‑retention identification, and the DROP workflow for end‑to‑end processing—to generate statistically robust outlier calls. By quantifying expression, splicing, and mono‑allelic expression changes, the system re‑classifies VUS into actionable findings and highlights previously overlooked pathogenic variants. Integrated prioritization algorithms score each variant across multiple omics layers, producing ranked candidate lists for review. The resulting reports are delivered through a secure web interface, enabling geneticists and diagnostic labs to incorporate RNA‑level evidence into their interpretation pipelines and improve diagnostic yield by up to 15 %.
Target Audience
Primary users are clinical geneticists, molecular diagnostic laboratories, and rare‑disease research programs that require functional validation of genomic variants for patient diagnosis and counseling.
Features
- Automated RNA‑seq preprocessing, alignment, and quality‑control pipeline optimized for clinical samples.
- OUTRIDER algorithm uses an autoencoder‑based model and negative‑binomial distribution to detect statistically significant expression outliers.
- FRASER 2.0 detects aberrant splicing and intron‑retention events, applying latent‑confounder correction and multiple‑testing adjustment.
- DROP workflow integrates expression, splicing, and mono‑allelic expression results with DNA variant calls and phenotypic metadata for unified interpretation.
- Prioritization engine combines multi‑omics scores into a ranked variant list, validated in the NIH‑funded CAGI challenge.
- Secure, cloud‑hosted analytics with encrypted data transfer and role‑based access controls, delivering downloadable HTML reports and interactive visualizations.
- Compatibility with standard clinical data formats (VCF, phenopacket) to streamline downstream reporting and EHR integration.