Random Access in DNA Storage: Algorithms, Constructions, and Bounds

TL;DR

This paper advances DNA data storage by developing algorithms for exact expected reads, deriving bounds, and proposing improved code constructions to optimize random access efficiency.

Contribution

It introduces a novel algorithm for computing expected reads with linear complexity and provides new code constructions and bounds that enhance random access performance in DNA storage.

Findings

Exact expected number of reads computed efficiently

Improved upper bounds for code constructions

Tighter lower bounds establishing optimality of simple parity code

Abstract

As DNA data storage moves closer to practical deployment, minimizing sequencing coverage depth is essential to reduce both operational costs and retrieval latency. This paper addresses the recently studied Random Access Problem, which evaluates the expected number of read samples required to recover a specific information strand from $n$ encoded strands. We propose a novel algorithm to compute the exact expected number of reads, achieving a computational complexity of $O(n)$ for fixed field size $q$ and information length $k$. Furthermore, we derive explicit formulas for the average and maximum expected number of reads, enabling an efficient search for optimal generator matrices under small parameters. Beyond theoretical analysis, we present new code constructions that improve the best-known upper bound from $0.8815k$ to $0.8811k$ for $k=3$, and achieve an upper bound of $0.8629k$ for $k=4$ for sufficiently large $q$. We also establish a tighter theoretical lower bound on the expected number of reads that improves upon state-of-the-art bounds. In particular, this bound establishes the optimality of the simple parity code for the case of $n=k+1$ across any alphabet $q$.