SynDe: Syndrome-guided Decoding of Raw Nanopore Reads
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arXiv
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| Format: | Preprint |
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2026
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| author | Banerjee, Anisha Sokolovskii, Roman Heinis, Thomas Wachter-Zeh, Antonia Rosnes, Eirik Amat, Alexandre Graell i |
| author_facet | Banerjee, Anisha Sokolovskii, Roman Heinis, Thomas Wachter-Zeh, Antonia Rosnes, Eirik Amat, Alexandre Graell i |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01054 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SynDe: Syndrome-guided Decoding of Raw Nanopore Reads Banerjee, Anisha Sokolovskii, Roman Heinis, Thomas Wachter-Zeh, Antonia Rosnes, Eirik Amat, Alexandre Graell i Information Theory 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. |
| title | SynDe: Syndrome-guided Decoding of Raw Nanopore Reads |
| topic | Information Theory |
| url | https://arxiv.org/abs/2604.01054 |