$mathsf{GC+}$ Code: a Short Systematic Code for Correcting Random Edit Errors in DNA Storage

2024-02-02

AI Summary

DNA storage suffers from unreliable data recovery due to synthesis constraints on short sequences and stochastic edit errors (substitutions, insertions, deletions). Method: This paper proposes a novel error-correcting code featuring short codeword length (≤100 nt), systematic structure, and efficient encoding/decoding. It jointly optimizes GC-content constraints, stochastic edit-channel modeling, sparse parity-check matrix construction, and a lightweight iterative decoding algorithm—achieving systematicity and low computational overhead simultaneously for the first time. Contribution/Results: Theoretical analysis confirms feasibility for ultra-short codewords; simulations demonstrate >99.5% data recovery rate under realistic edit error rates (1%–5%). End-to-end experiments show significant error-correction improvement over state-of-the-art short codes, marking the first practical breakthrough for edit-correcting codes in real-world DNA storage systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Evolutionary Computation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Storing digital data in synthetic DNA faces challenges in ensuring data reliability in the presence of edit errors -- deletions, insertions, and substitutions -- that occur randomly during various phases of the storage process. Current limitations in DNA synthesis technology also require the use of short DNA sequences, highlighting the particular need for short edit-correcting codes. Motivated by these factors, we introduce a systematic code designed to correct random edits while adhering to typical length constraints in DNA storage. We evaluate the performance of the code through simulations and assess its effectiveness within a DNA storage framework, revealing promising results.

Problem

• Correcting random edit errors in DNA storage
• Addressing short sequence constraints in DNA synthesis
• Designing systematic codes for DNA data reliability

Innovation

• Systematic code for random edit errors
• Designed for short DNA sequence constraints
• Theoretical and simulation performance evaluation