Nanopore sequencing reads DNA by threading a strand through a protein pore and measuring the ionic current as each nucleotide passes. The current shifts with each base, producing a noisy signal that a basecaller converts into sequence. Errors in the raw signal propagate through every downstream analysis — alignment, variant calling, methylation detection.
CERN operates upstream of the basecaller. It applies a hidden Markov model to the raw current trace, learning the characteristic error patterns — signal drifts, level transitions that are too fast to resolve, systematic biases from the pore chemistry — and correcting them before the basecaller sees the signal. The improvement in mapping accuracy cascades: cleaner signal, cleaner bases, cleaner alignment.
The approach survives changes in nanopore chemistry. As the sequencing platform evolves — new pore proteins, new motor enzymes, new flow cell designs — the raw signal changes character. CERN trains on reference data for each chemistry, learning the specific error model rather than assuming a universal one. The correction adapts to the instrument.
Working on the raw signal rather than the basecalled sequence means the correction preserves information that basecalling discards. A basecalled 'A' is a symbol. The raw current level that generated it is a measurement — it contains not just the identity of the base but the modification state, the local sequence context, the pore-to-pore variation. Correcting at the signal level keeps this information available for analyses that need it.
The earlier the correction, the more information survives.