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Genomes in Training Data

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Variant Classification Accuracy

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Faster Guide RNA Design

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Gene Therapy Programs Supported

The Problem

Genomic R&D is
Still a Bottleneck

Interpreting billions of variants, designing effective gene therapies, and identifying druggable targets requires years of manual analysis. Most of the genomic search space remains unexplored.

Millions of Variants of Unknown Significance

Clinical genomics labs face a growing backlog of VUS classifications. Manual curation cannot keep pace with sequencing throughput, leaving patients without answers.

Gene Therapy Design is Trial and Error

AAV capsid engineering and guide RNA design rely on expensive screening campaigns. Most candidates fail to achieve the required tissue tropism or editing efficiency.

$10M+ Per Gene Therapy Program

Vector optimization, off-target analysis, and regulatory submissions consume years and millions. AI-driven design can compress timelines from years to weeks.

How It Works

The XineGenomics
Analysis Loop

Upload Genomic Data

Import whole-genome sequencing data from Illumina, PacBio, or Nanopore platforms. Automated QC, alignment, and variant calling with GPU-accelerated pipelines.

AI Interprets Variants

Our genomic foundation model classifies variants by pathogenicity, predicts splicing effects, and scores regulatory impact — all without manual curation.

Design Therapeutics

Optimal CRISPR guide RNAs, AAV capsid variants, and LNP formulations are designed with AI-predicted efficacy, specificity, and tissue tropism scores.

Validate & Export

Off-target analysis, delivery optimization, and regulatory-ready reports are generated automatically. Export constructs for synthesis and preclinical testing.

Genomic Foundation Model

A Foundation Model for Genomes

Our genomic foundation model is trained on 500K+ whole genomes, learning the grammar of genetic variation and its functional consequences. It enables zero-shot variant effect prediction across coding and non-coding regions.

Zero-shot variant effect prediction — predict pathogenicity and functional impact of any variant without requiring disease-specific training data

Regulatory element detection — identify enhancers, silencers, and regulatory variants in non-coding regions with nucleotide-level resolution

Population-aware scoring — variant frequency and effect predictions contextualized across diverse ancestral populations

500K+ genomes trained — foundation model captures rare variant effects invisible to smaller datasets

CRISPR Guide RNA Designer

Purpose-Built for Gene Editing

A specialized module for designing optimal CRISPR guide RNAs. Predict on-target efficiency, minimize off-target effects, and score editing outcomes — all computationally.

Optimal guide design — generate and rank guide RNAs by predicted on-target editing efficiency across Cas9, Cas12, and base editor systems

Off-target prediction — genome-wide off-target site identification with mismatched and bulge tolerance scoring

Editing outcome prediction — predict indel profiles, base editing windows, and prime editing pegRNA designs

Multiplex design — design guide RNA libraries for multiplexed editing campaigns with minimized cross-reactivity

Gene Therapy Vectors

From Genome to Gene Therapy in One Platform

Design and optimize gene therapy delivery vectors with AI-predicted tissue tropism, transduction efficiency, and immunogenicity profiles.

AAV capsid engineering — directed evolution in silico with AI-predicted tissue tropism and neutralizing antibody evasion

LNP formulation optimization — lipid nanoparticle composition optimization for mRNA and siRNA delivery with organ-specific targeting

Payload design — transgene cassette optimization including promoter selection, codon optimization, and regulatory element placement

Immunogenicity prediction — predict anti-capsid immune responses and design immune-evasive vector variants

Review

Built for Genomic Decision Support

XineGenomics surfaces candidate interpretations and therapy designs with evidence trails for expert review.

Variant Evidence

Pathogenicity scores are paired with annotations, population context, and model confidence.

Guide Prioritization

CRISPR designs are ranked by efficacy, specificity, editing window, and off-target risk.

Regulatory Reports

Exports summarize rationale, assumptions, and validation needs for downstream documentation.

Applications

Genomic Programs We Support

Rare Disease

Prioritize causal variants and therapeutic hypotheses for unresolved cases.

CRISPR

Design guide RNAs across Cas systems with off-target analysis.

AAV Vectors

Engineer capsids for tissue tropism, potency, and immune evasion.

Target Discovery

Connect genomic signals to druggable mechanisms and validation plans.

Security

Designed for Sensitive Genomic Data

Genomics workflows demand strict data isolation, auditability, and access control from upload to export.

Encrypted genomic data storage and transfer

Workspace controls for projects, cohorts, and collaborators

Audit trails for variant review and therapeutic design decisions

FAQ

XineGenomics FAQ

Which sequencing platforms are supported?+

Workflows support common inputs from short-read and long-read sequencing pipelines, including variant and alignment files.

Can teams review AI classifications?+

Yes. Variant classifications include evidence, confidence, and annotations for expert review.

Does it support therapy design?+

Yes. The platform supports CRISPR guide, AAV capsid, LNP formulation, and payload optimization workflows.

Decode Genomes
With Intelligence