Can a neural net sort DNA from the nanopore’s raw current?
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.
Source
SquiggleNet: real-time, direct classification of nanopore signals
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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Can a neural net sort DNA from the nanopore’s raw current?
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Adaptor-cleaned traces: 89.35% accuracy, 90% TPR, 86.9% TNR.
89.35% accuracy, 90% true positive rate, and 86.9% true negative rate
Respiratory metagenome: 90.8% accuracy, 72.5% TPR, 90.9% TNR.
90.8% overall accuracy in the Respiratory Metagenome dataset, 72.5% true positive rate, and 90.9% true negative rate
What they did
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.
What they found
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.
The limits
What it doesn't show
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.
Key terms
- Squiggle
- Time series of ionic current as DNA translocates a nanopore.
- Read until / ejection
- Rejecting off-target molecules mid-pore once classified.
- ResNet-style 1D CNN
- SquiggleNet’s architecture: residual bottleneck blocks over the current trace.
- HeLa&Zymo
- Training mix of HeLa DNA plus an 8-species Zymo bacterial standard.
- TPR / TNR
- True positive (bacterial) and true negative (human) rates.
- LINEs
- Human long interspersed repeats that SquiggleNet still classified as human.
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SquiggleNet classifies from:
Common questions
How much signal is needed?
About 1 second of sequencing data per read.
Human vs bacterial accuracy?
Over 90%.
Unseen bacteria in a metagenome?
90.8% overall accuracy (72.5% TPR, 90.9% TNR).
Architecture?
1D convolutions with ResNet-style residual blocks.
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