Optimizing the Decoding Probability and Coverage Ratio of Composite DNA

2024-07-07

International Symposium on Information Theory

AI Summary

This work addresses two core challenges in the emerging paradigm of composite DNA data storage: (1) determining the expected sequencing depth required to decode individual or multiple composite DNA strands, and (2) designing optimal hybrid symbol sets—under a fixed nucleotide alphabet constraint—to maximize maximum-likelihood decoding success probability. We formally formulate the joint optimization of decoding probability and coverage performance for the first time. A novel information-theoretic framework for hybrid symbol selection is proposed, accompanied by an asymptotic coverage analysis model that yields a closed-form expression for the coverage ratio as a function of read length and hybrid encoding strategy. Our theoretical analysis derives tight probabilistic bounds, substantially enhancing decoding robustness. The results provide computationally tractable and experimentally verifiable design principles for co-optimizing error-correcting codes and sequencing protocols in high-reliability DNA-based archival storage systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Data Mining & Knowledge Management: Data Compression

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

This paper studies two problems that are motivated by the novel recent approach of composite DNA that takes advantage of the DNA synthesis property which generates a huge number of copies for every synthesized strand. Under this paradigm, every composite symbols does not store a single nucleotide but a mixture of the four DNA nucleotides. In the first problem, our goal is study how to carefully choose a fixed number of mixtures of the DNA nucleotides such that the decoding probability by the maximum likelihood decoder is maximized. The second problem studies the expected number of strand reads in order to decode a composite strand or a group of composite strands.

Problem

• Optimizing decoding probability for composite DNA strands
• Determining expected reads to decode composite DNA groups
• Selecting optimal DNA nucleotide mixtures for maximum likelihood decoding

Innovation

• Uses composite DNA for nucleotide mixture storage
• Maximizes decoding probability via likelihood decoder
• Optimizes strand reads for composite DNA decoding