Sequencing Coverage Analysis for Combinatorial DNA-Based Storage Systems

This study introduces a novel model for analyzing and determining the required sequencing coverage in DNA-based data storage, focusing on combinatorial DNA encoding. We seek to characterize the distribution of the number of sequencing reads required for message reconstruction. We use a variant of the coupon collector distribution for this purpose. For any given number of observed reads, $R\in \mathbb {N}$ , we use a Markov Chain representation of the process to compute the probability of error-free reconstruction. We develop theoretical bounds on the decoding probability and use empirical simulations to validate these bounds and assess tightness. This work contributes to understanding sequencing coverage in DNA-based data storage, offering insights into decoding complexity, error correction, and sequence reconstruction. We provide a Python package, with its input being the code design and other message parameters, all of which are denoted as $\boldsymbol {\Theta }$ , and a desired confidence level $1-\delta $ . This package computes the required read coverage, guaranteeing the message reconstruction $R=R(\delta,\boldsymbol {\Theta })$ .