Proposed chemistry · Photon Oxidation
From raw ionic signal to a clinical-grade break map. We propose to use our novel photon-oxidation chemistry to mark every DNA break; nanopore sequencing and purpose-built AI resolve them at base resolution — single- and double-strand.
Every break leaves a unique ionic signature as it passes through the pore. Our models learn the signatures and call breaks in real time. Base resolution. Native DNA. No amplification.
Our end-to-end pipeline takes raw nanopore signal and — through a stack of deep-learning models — produces base-resolution maps of DNA damage.
High-MW DNA extraction, optional enzymatic labeling of break ends.
Long reads on MinION / PromethION; raw ionic current captured live.
Transformer-based basecaller translates squiggles into sequence + quality.
AI · Deep LearningProprietary CNN + signal-level classifier detects SSBs and DSBs at base resolution.
AI · In2Cell CoreAlignment, duplicate removal, context annotation across reference genomes.
Break landscapes, motif context, hotspot calls — exported as HTML / PDF.
Long reads need long DNA. We optimize lysis and bead cleanup to preserve fragments > 50 kb, with optional enzymatic labelling to flag damaged ends before sequencing.
Tissue (fresh / FFPE), cell pellets, primary cells, organoids
Quality-checked DNA library, 312 ng target
DNA passes through a protein nanopore. Ionic current disturbances encode the sequence. Subtler signals reveal breaks, modifications, and damage.
Oxford Nanopore MinION / PromethION
~1.2M reads per flow cell, streaming
Our transformer basecaller is fine-tuned on break-containing reads, lifting recall around damage sites by ~30% vs stock Guppy / Dorado while maintaining Q scores.
Transformer, 120M parameters
12M break-labelled reads
The signal-level CNN reads raw squiggle in parallel with basecalls, classifying every position as SSB, DSB, complex lesion, or intact. Per-read confidence scores feed downstream.
> 92% recall on DSB calls
> 98%, per-base
Break calls are aligned to reference, annotated with genomic context (gene, exon, repeat, chromatin state, replication timing) via our ContextNet graph model.
GRCh38, T2T-CHM13, mouse mm39
~8 min per 30x genome
Break landscapes, hotspot calls, motif contexts, and repair-outcome signatures. Delivered as an interactive HTML or PDF that non-specialists can action.
HTML, PDF, VCF, BED
~24 hours, sample-to-report
From sample intake through basecalling and the POLEX Core simulation, each read is classified as SSB, DSB or complex, correlated against the tissue reference database, and delivered as an evidence-based clinical recommendation.
Transformer basecaller fine-tuned on break-containing reads, higher recall around damage sites.
Signal-level CNN reading raw squiggle alongside basecalls to flag SSBs and DSBs with per-read confidence.
Graph model placing each break in its genomic neighbourhood: motifs, chromatin, replication timing.