Understand what a genetic change means

GeneGenius helps research and lab teams assemble variant annotations, literature and ACMG/AMP evidence into a traceable research report.

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NVIDIA Inception member

For research use only. Not intended for diagnosis, treatment, or clinical decision-making.

The Rare Disease Bottleneck

Why this matters now

Rare disease diagnosis can take years, while the cost of delayed answers continues to rise. At the same time, genomic data is growing faster than specialist teams can interpret it. The annual U.S. economic burden of rare disease is estimated to approach $1 trillion on 2019 data.

30M+: Americans living with a rare disease — roughly 1 in 10; NORD Rare Disease Facts (2026)
5+ yrs: Typical time to diagnosis; NORD Rare Disease Facts (2026)
$517K: Upper bound of avoidable cost per patient from delayed diagnosis ($86K–$517K across seven rare diseases); EveryLife Delayed Diagnosis Study (2023)

See the full picture — the bottleneck and how we close it

The GeneGenius Workflow

From genomic data to research-ready evidence

GeneGenius helps research teams analyze genetic variants, connect findings to supporting evidence, and generate structured reports in one traceable workflow.

01Genomic Data
02Variant Analysis
03AI-Assisted Interpretation
04Evidence Review
05Research Report

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Product Walkthrough

See GeneGenius in action

Watch how GeneGenius turns complex DNA data into clear, evidence-backed insights and a research-ready report.

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Analysis dimensions

How we interpret genomic data

Nine signals we combine when interpreting a variant. Not every dimension is independently validated or live yet.

ACMG/AMP-aligned classification

Variant classification led by core signals (e.g. PVS1, PS1, PM2) rather than a fixed criteria checklist.

Structural & binding impact

Model-predicted binding delta from protein-structure models — a computational signal, not experimental binding energy.

Splicing impact

SpliceAI-based scoring of potential splice disruption around each variant.

Expression outlier

Outlier expression relative to normal tissue, using GTEx baselines for context.

Phenotype-to-gene

Prioritization from synthetic clinical notes and HPO terms. Real-world EHR validation is in progress.

Therapeutic context

Therapeutic research context from CIViC, ClinicalTrials.gov, PharmGKB, and OpenFDA labels — informational, not treatment guidance.

Metabolomic overlay

Pathway-level context layered on findings via KEGG.

Functional rectification

Correcting annotation miscalls to reduce noise carried into interpretation.

Variant-to-mechanism

After the report, mechanism links grounded in the published literature.

See who we build this for

What we do

From a raw VCF or FASTQ to an interpretable, evidence-linked report — in minutes

We start where sequencing ends. GeneGenius is the interpretation layer in the middle — not the lab, not the clinician — turning genomic data into research-ready evidence for the teams who rely on it.

For Clinicians

Automated variant interpretation with evidence-linked, citation-backed research reports.

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For Labs

High-throughput VCF or FASTQ to ACMG/AMP-aligned classification, built for reproducibility across samples.

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For Researchers

Multi-omics annotation, pathogenicity prediction, and literature-grounded evidence at scale.

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Research Use Only — not for diagnostic procedures

Organizational Value

Built for more genomic capacity with the teams you have.

GeneGenius is designed to help laboratories and research programs complete more genomic work with existing teams, keep more analysis in-house, and expand the services they can offer.

Capacity

Built for throughput

Designed to help teams complete more genomic analyses without increasing specialist headcount at the same rate.

Retention

Keep work in-house

Designed to reduce dependence on outsourced interpretation and keep more genomic work within the organization.

Services

Room to expand services

Built to support reanalysis, cohort research, evidence reporting, and new genomic research programs.

For hospitals and academic medical centers, this can also support larger research cohorts, biobank programs, sponsored studies, and pharmaceutical partnerships.