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arXiv · 2604.01054

SynDe: Syndrome-guided Decoding of Raw Nanopore Reads

Anisha Banerjee·Roman Sokolovskii·Thomas Heinis·Antonia Wachter-Zeh·Eirik Rosnes·Alexandre Graell i Amat
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Abstract

Nanopore sequencing technology remains highly error-prone, making efficient error correction essential in DNA-based data storage. Prior work addressed high error rates using convolutional codes with their decoder coupled with the basecaller, but such approaches only accommodate a limited number of code classes and incur significant decoding complexity. To overcome these limitations, we propose two algorithms: PrimerSeeker, which efficiently detects primer sequences in raw nanopore sequencing reads, and SynDe, a decoder that operates on the same raw reads and supports any linear error correction code with a low-complexity graphical representation. PrimerSeeker provides primer location estimates close to those of existing approaches while being better suited for real-time primer detection during sequencing. SynDe performs well with convolutional codes augmented with periodic markers, often approaching or exceeding the performance of existing algorithms with a lower time complexity. Remarkably, the confidence scores produced by SynDe reliably identify which of its outputs should be discarded.

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Anisha Banerjee, Roman Sokolovskii, Thomas Heinis, Antonia Wachter-Zeh, Eirik Rosnes, Alexandre Graell i Amat. 2026-04-01. SynDe: Syndrome-guided Decoding of Raw Nanopore Reads. https://arxiv.org/abs/2604.01054

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