Agentic discovery platform

Multi-omics insights, powered by 100% of the genome

Lucid combines proprietary genomic variant detection, Dark Genome expertise, and multi-omics analysis - including transcriptomics and proteomics - to map 3D genome architecture and link regulatory elements to target genes.

We uncover genomics-driven disease mechanisms, enabling novel biomarkers, target discovery, and patient stratification.

Only 1 in 10 drugs makes it to market

Genomics-guided drugs are 2.6× more likely to reach approval, raising success rates from under 10% to around 20%.

Lucid's team has been at the forefront of non-coding and structural variant research, well before these regions gained wider recognition. We bring deep expertise in uncovering regulatory drivers that conventional pipelines miss.

Why genomics-driven trials?

FASTER & CHEAPER

40%

Reduction in sample size

By including genomic biomarkers in trials while maintaining statistical power

MORE SUCCESSFUL

2.6x

Improved market approval rate

Based on patient stratification and identification of super and non-responders

NEW MARKET OPPORTUNITY

60%

Cancer trials already include genomics

Other therapeutic areas are quickly catching up with oncology in using genomics in trials

Our solutions

Genomics-driven
biomarker discovery

Uncover novel, functional biomarkers including non-coding and structurally complex genomic regions that are inaccessible to standard analytical pipelines.

Leverage freedom to operate in a largely unpatented space of non-coding biomarkers

Uncover disease mechanisms invisible to coding-only approaches (neglecting the Dark Genome)

Identify regulatory elements driving disease initiation, progression and resistance

Genomics-driven
patient stratification

Whole genome analysis of patients to identify potential super and non-responders for clinical trials to adjust sample setup and to boost trial success likelihood

Reduce sample size of clinical studies while maintaining statistical power

Identify Super and Non-responders to increase clinical trial success likelihood

Enable genomic driven adaptive trial arms to reduce trial operations costs

Genomics-driven
Target Identification

Understand complex disease mechanisms and validate novel drug targets by mapping the complete regulatory architecture of the genome.

Understand variant-to-function pathways to prioritize real causal drivers

Discover targets in previously overlooked genomic regions (largely patent free space)

De-risk target selection with stronger genetic evidence (GWAS and functional genomics)

Technology-agnostic analysis

We support all sequencing technologies and apply our proprietary AI data cleaning model to aggregate and filter out background noise.

  • Short-reads

    e.g. Illumina

  • Long-reads

    e.g. PacBio

  • Linked-reads

    e.g TELL-Seq

  • Optical mapping

    e.g. Bionano

Multi-omics analysis across
data types

Our platform analyzes data from panels to genomes, providing a manageable, prioritized list of suggested causative pathogenic mutations.

  • Whole genomes

  • Gene panels

  • Exomes

  • RNA-seq / Transcriptomics

  • Hi-C

  • ChIP-seq

  • Proteomics

  • EHR data

Case study

A non-coding deleted DNA segment downstream of PITX2 disrupted a TAD boundary element, leading to atrial-specific misexpression and atrial fibrillation. The regulatory mechanism in vivo highlights the impact of non-coding structural variants on cardiac rhythm.

Step 1: Cohort Analysis

Several overlapping structural variants located ~800kb downstream of the PITX2 gene, a known cardiac development factor, were identified in whole-genome sequencing data from atrial fibrillation patients. These variants were absent in SNP-array data.

Step 2: Mechanism of Action

A non-coding CTCF/TAD-boundary deletion fuses adjacent TADs and rewires long-range contacts, leading to PITX2 misexpression

Step 3: In Vivo Validation

The direct causal link between the structural variant and atrial fibrillation susceptibility was subsequently confirmed in a targeted mouse model, validating the finding from discovery to function.

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