VUS DISCOVERY MODULE — NOW IN EARLY ACCESS

Your VUS backlog is
growing.

Your interpretation tools
are not.

The RKE Platform’s VUS Discovery Module is entering a limited founding beta
for genomics professionals who want to discover multidimensional
relationship scenarios that conventional interpretation tools may never
surface.
3,172
Relationship candidates surfaced in RA study
Peer-reviewed publications
6
Dark-data relationship signals
12/12
Patient samples with MUC20 signal
Peer-reviewed and published in the Journal of Molecular Diagnostics — Endometriosis (July 2024) · Rheumatoid Arthritis (April 2025)
THE VUS BOTTLENECK

Sequencing got cheap.

Interpretation didn't.

Whole-exome sequencing now produces thousands of variants per patient. Many remain classified as Variants of Uncertain Significance — and the backlog grows faster than teams can develop enough evidence to interpret them.

Standard AI and bioinformatics tools often depend on large, clean, normalized datasets. Real-world genomic discovery frequently depends on small, wide, sparse evidence where the most valuable signal appears as an unexpected relationship scenario.

The VUS backlog requires more than another prediction score. It requires a way to reveal contextually relevant relationship scenarios that researchers can evaluate as potential evidence pathways.

10–15

Years — industry consensus time to reclassify the current VUS backlog (ACMG 2024)

>6,000

Genotype-phenotype relationship space with potential RA relevance — much of it invisible to conventional tools

100%

Dark-data signal rate — in published studies, relationship evidence surfaced by the RKE engine was missed entirely by conventional bioinformatics and AI platforms

DISCOVERY MODEL

A biomimetic digital

twin ecosystem

Unlike statistical prediction tools, the VUS Discovery Module focuses on differentiated results: multidimensional relationship scenarios that connect genotype, phenotype, and patient context in ways conventional tools often cannot reveal. The goal is not to explain processing. The goal is to help researchers see new evidence pathways.

🧬
DIGITAL TWIN 01

Genotype

Upload patient exome CSV files. The engine discovers multidimensional relationship scenarios across your data, and ranks the scenarios by prevalence — no normalization required.

🫀
DIGITAL TWIN 02

Phenotype

Define the phenotype context you believe matters. The module reveals relationship scenarios that connect variants to the disease-relevant dimensions you want to evaluate.

🧑‍⚕️
DIGITAL TWIN 03

Patient

Add patient-context dimensions when relevant to reveal broader relationship scenarios across clinical, phenotypic, and genomic evidence.

01 — DEFINE
Frame the discovery question

Start with the phenotype, cohort, or VUS interpretation challenge you want to explore.

02 — SELECT
Choose relevant dimensions

Bring your domain expertise to the evidence dimensions that may shape interpretation.

03 — DISCOVER
Reveal relationship scenarios

See patterns and candidate relationships that may be invisible to standard approaches.

04 — PRIORITIZE
Rank evidence pathways

Review scenarios by prevalence, contextual relevance, and discovery potential.

05 — REVIEW
Evaluate for research use

Use the results to guide hypothesis development, evidence review, and future validation.

WHY IT'S DIFFERENT

Not a prediction engine.

A discovery engine.

STANDARD AI & BIOINFORMATICS PLATFORMS

Depends on large, normalized datasets — rare disease and small-cohort questions are often under-served

Can miss unexpected relationship signals — the most important discovery cues may sit outside standard assumptions

Produces prediction scores rather than contextually relevant relationship scenarios

Limited evidence transparency — difficult to evaluate why a relationship matters

Variant consequence focus — limited ability to reveal broader genotype-phenotype-patient relationship scenarios

RYLTI RKE — VUS DISCOVERY MODULE

Reveals scenarios in small, wide, sparse evidence — useful where conventional tools may lack sufficient signal

Finds signal in unexpected places — dark-data relationships become visible for expert review

Discovery scenarios, not predictions — results are framed for expert evaluation, not automated classification

Reproducible discovery evidence — scenarios can be revisited, reviewed, and compared across research questions

Contextual relationship evidence — supports stronger hypotheses for future interpretation and validation work

PEER-REVIEWED EVIDENCE

Published before it launched.

Validated in the field.

JOURNAL OF MOLECULAR DIAGNOSTICS — APRIL 2025

Biomimetic Digital Twins and Multiomics Applications to Rheumatoid Arthritis and VUS Reclassification

Kearns WG, Glick J, Baisch L, et al. — Genzeva, LumaGene, RYLTI, Qiagen Digital Insights.
25 patient samples · 25 controls · Whole-exome NGS

RA finding: 3,172 VUS-related relationship candidates were surfaced in patient samples. Dark-data relationship signal emerged across 6 genes — HIF1A, HLA-DOA, PTGER3, HIPK3, TGFBR3, HIF1A-AS3 — beyond what standard bioinformatics platforms revealed.

JOURNAL OF MOLECULAR DIAGNOSTICS — JULY 2024

Knowledge Engineering via Biomimetic Digital Twin Ecosystem, Phenotype-Driven Variant Analysis, and Exome Sequencing

Kearns WG, Stamoulis G, Glick J, et al. — Genzeva, Qiagen, RYLTI, Brigham & Women’s Hospital / Harvard.
12 patient samples · matched controls · Endometriosis

Endometriosis finding: a VUS-related relationship scenario involving MUC20 appeared across all 12 patient samples, suggesting a potential biomarker path for future diagnostic research. A chromosomal hotspot was also reported on the short arm of chromosome 8.

WHO IT'S BUILT FOR

Three audiences.

One accelerated evidence engine.

PHARMA & BIOTECH

De-risk drug targets before Phase I

Discover multidimensional relationship scenarios relevant to your target phenotype — even when large population cohorts or conventional normalized datasets are unavailable.

CLINICAL LABORATORIES

Turn a VUS into an actionable variant

Clinical lab directors face immense pressure to evaluate VUSs. The module helps surface defendable relationship scenarios — not statistical predictions — for expert review and future evidence development.

ACADEMIC RESEARCHERS

Publication-ready causal insights, faster

Two peer-reviewed publications in the Journal of Molecular Diagnostics demonstrate what becomes possible when discovery is organized around multidimensional relationship scenarios rather than prediction alone.

FOUNDING BETA PROGRAM

Not just early access.

Early influence.

We are inviting a limited cohort of genomics professionals to evaluate the VUS Discovery Module, provide feedback, and help shape how multidimensional relationship scenarios are presented for real-world research and interpretation use.

WHY JOIN

Founding researcher benefits

WHO QUALIFIES

Preferred beta participants

BETA COHORT

Limited founding cohort

Target founding cohort: 50 genomics professionals. We will prioritize applicants with active research questions, VUS interpretation needs, or strong feedback potential.

FUTURE ACCESS PLANS

Beta access is complimentary.

Plans scale after launch.

During the founding beta, accepted participants receive complimentary access. The tiers below preview how access may be structured after beta based on evaluation, proof-of-concept, and professional research use.

FREE
$0

For learning, evaluation, and sample-data exploration.

BETA FOCUS
PAA DEMO
Complimentary in Beta

For proof-of-concept studies focused on your own discovery questions.

PROFESSIONAL
Future Plan

For clinical, academic, and commercial research teams.

All plans lead to one action during beta: Apply for Founding Beta Access.