Optimizing Sequencing Coverage Depth in DNA Storage: Insights From DNA Storage Data

TL;DR

This paper investigates how to optimize DNA sequencing coverage depth in DNA storage systems by analyzing real experimental data, extending previous theoretical models to more realistic log-normal distribution channels, and providing practical insights.

Contribution

It introduces a novel analysis of DNA storage coverage depth using real data, extending models from uniform to log-normal channels, and explores both noiseless and noisy scenarios.

Findings

Positive correlation between MDS code rate and minimum coverage depth

Decoding success probability varies with code rate and sample size

Extended lower bounds for coverage depth in log-normal noisy channels

Abstract

DNA storage is now being considered as a new archival storage method for its durability and high information density, but still facing some challenges like high costs and low throughput. By reducing sequencing sample size for decoding digital data, minimizing DNA coverage depth helps lower both costs and system latency. Previous studies have mainly focused on minimizing coverage depth in uniform distribution channels under theoretical assumptions. In contrast, our work uses real DNA storage experimental data to extend this problem to log-normal distribution channels, a conclusion derived from our PCR and sequencing data analysis. In this framework, we investigate both noiseless and noisy channels. We first demonstrate a detailed positive correlation between MDS code rate and the expected minimum sequencing coverage depth. Moreover, we observe that the probability of successfully decoding all information in a single sequencing run decreases and then increases as code rate rises, when the sample size is optimized for complete decoding. Then we extend the lower bounds of the DNA coverage depth from uniform to log-normal noisy channels. The findings of this study provide valuable insights for the efficient execution of DNA storage experiments.