Distributed dna storage reduces reading time and cost depending on code and partition
The Coverage Depth Problem in Distributed DNA Data Storage
Information Theory
Summary
Reading information stored in DNA often involves reading the same pieces repeatedly, which slows things down and costs more. The authors studied how dividing DNA data into separate containers affects the time and cost to read it all. They found exact ways to calculate how long it takes to recover the full message, and showed that some codes are best at minimizing reading cost. Their work explains when dividing data lowers only the waiting time and when it also saves money.
What this means in practice
- •For dna storage engineers: Optimize data encoding and container partitioning to reduce total DNA sequencing reads and retrieval time.
- •For data center architects: Design distributed storage systems to balance sequencing cost and latency by applying container-based sampling strategies.
A theory result. No direct application yet.
Authors
Xiangliang Kong, Ohad Elishco, Chen Wang, Tolga M. Duman
Abstract
Random sampling in DNA sequencing produces repeated reads, increasing retrieval latency and sequencing cost. We study the coverage-depth problem for full-message recovery in distributed DNA storage under noiseless uniform sampling, where strands are partitioned among $M$ containers and one strand is independently sampled with replacement from each container per round. For arbitrary linear codes and ordered partitions, we derive exact formulas for the recovery-time distribution and expectation. We prove that MDS codes, whenever they exist, are optimal for every fixed partition, and establish a universal lower bound on the expected total read cost together with its equality conditions. For MDS codes, we identify container-size regimes that yield genuine savings in total reads and regimes that provide only parallelism without changing the asymptotic sequencing cost. For simplex codes, we prove that the $q$-ary simplex code is, up to isomorphism, the unique single-container minimizer among codes with the same parameters, resolving a recent conjecture by Bertuzzo, Ravagnani, and Yaakobi. We further construct a partition attaining the minimum total read cost and derive bounds for intermediate and balanced partitions. These results clarify when distributed sampling reduces latency alone and when it also reduces sequencing cost.