Gene expression
Can a neural net sort DNA from the nanopore’s raw current?
Open access · cc by · source: Europe PMC
SquiggleNet, a ResNet-style 1D CNN, classifies Oxford Nanopore reads from ~1 s of raw current faster than DNA translocates, beating base-calling-plus-alignment and exceeding 90% accuracy on human vs bacterial DNA.
Study at a glance
- Design
- Computational / modelling — SquiggleNet: ResNet-style 1D CNN classifying raw Oxford Nanopore electrical signals (squiggles) vs base-calling + alignment; four mixed human/bacterial datasets
- N
- N=4 · Four experimental mixed human/bacterial datasets (HeLa&Zymo plus three Human&Zymo/metagenome sets); 1 s of signal per read
- Population
- Nanopore electrical traces from human (HeLa/human DNA, LINEs) and bacterial (Zymo mock, respiratory metagenome) samples
- Outcome
- Species-class accuracy, TPR/TNR, speed/memory vs alignment, and generalization to unseen bacteria
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Key findings
With adaptor/barcode noise removed, accuracy reached 89.35% (90% TPR, 86.9% TNR). Human vs bacterial DNA exceeded 90% accuracy; the respiratory metagenome hit 90.8% overall (72.5% TPR, 90.9% TNR, AUROC 0.817) despite unseen species. The model used ~10× less memory than alignment and ran faster than pore translocation.
Methodology
Trained a four-block residual 1D CNN on squiggles, compared it with base-calling then Minimap2 alignment, and tested four mixed human/bacterial datasets including HeLa&Zymo (8 Zymo species) and a respiratory metagenome with unseen bacteria.
Limitations
A binary human-vs-bacteria classifier is not full species ID or clinical diagnostic clearance; accuracy drops when the bacterial fraction is tiny, and training still needs labeled squiggles.
How this study connects
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