SynDe: Syndrome-guided Decoding of Raw Nanopore Reads

TL;DR

SynDe introduces a low-complexity decoding algorithm for raw nanopore reads that supports any linear error correction code, improving error correction efficiency in DNA data storage.

Contribution

The paper presents SynDe, a novel decoder compatible with various linear codes and capable of real-time operation on raw nanopore sequencing data.

Findings

SynDe performs well with convolutional codes augmented with periodic markers.

SynDe's confidence scores reliably identify discardable outputs.

PrimerSeeker efficiently detects primer sequences with accuracy comparable to existing methods.

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.