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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Effective IDS Error Correction Algorithms for DNA Storage Channels With Multiple Output Sequences.

Caiyun Deng, Guojun Han, Pengchao Han

IEEE Transactions on Nanobioscience

|April 8, 2025

Summary

New DNA data storage error correction algorithms improve performance by reducing bit-error rates. These methods address challenges in DNA synthesis and sequencing, enhancing data integrity for long-term storage solutions.

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Area of Science:

Bioinformatics and Computational Biology
Data Storage Technologies
Information Theory

Background:

DNA data storage offers high density, replicability, and longevity.
Insertion, deletion, and substitution (IDS) errors in DNA synthesis/sequencing channels degrade storage performance.
Existing decoding methods struggle with multiple output sequences common in IDS channels.

Purpose of the Study:

To investigate effective IDS error correction algorithms for DNA data storage.
To compare the performance of two encoding schemes: marker codes (MC) and embedded marker codes (EMC), both with low-density parity-check (LDPC) outer codes.
To develop novel decoding algorithms to mitigate IDS errors and improve data integrity.

Main Methods:

Proposed the segmented progressive matching (SPM) algorithm for consensus sequence inference from multiple outputs.
Developed a synchronous decoding algorithm based on the Hidden Markov Model (SDH) for MC inner codes.
Developed an iterative external decoding (IED) algorithm integrating synchronous decoding and embedded normalized min-sum decoding (ENMS) for EMC inner codes.

Main Results:

The SDH algorithm with MC offers a lightweight solution for DNA data storage.
The IED algorithm with EMC provides superior decoding performance with linear complexity.
Proposed algorithms reduced bit-error rate (BER) by 21.72% to 99.75% compared to existing methods.

Conclusions:

Novel decoding algorithms significantly enhance the reliability of DNA data storage.
The choice between SDH/MC and IED/EMC depends on the trade-off between computational complexity and decoding performance.
These advancements pave the way for more robust and efficient DNA-based data archiving.