An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore-Sequenced Reads

We consider error-correcting coding for deoxyribonucleic acid (DNA)-based storage using nanopore sequencing. We model the DNA storage channel as a sampling noise channel where the input data is chunked into $M$ short DNA strands, which are copied a random number of times, and the channel outputs a random selection of $N$ noisy DNA strands. The retrieved DNA reads are prone to strand-dependent insertion, deletion, and substitution (IDS) errors. We construct an index-based concatenated coding scheme, i.e., the concatenation of an outer code, an index code, and an inner code. We further propose a low-complexity (linear in $N$ ) maximum a posteriori probability decoder that takes into account the strand-dependent IDS errors and the randomness of the drawing to infer symbolwise a posteriori probabilities for the outer decoder. We present Monte-Carlo simulations for information-outage probabilities and frame error rates for different channel setups on experimental data. We finally evaluate the overall system performance using the read/write cost trade-off. A powerful combination of tailored channel modeling and soft information processing allows us to achieve excellent performance even with error-prone nanopore-sequenced reads outperforming state-of-the-art schemes.