Channel Model with Memory for DNA Data Storage with Nanopore Sequencing
Résumé
This paper is dedicated to channel modeling and error-correction coding for DNA data storage with nanopore sequencing. We first propose a novel statistical model for DNA storage, which takes into account the memory within DNA storage error events, and follows the way nanopore sequencing works. Compared to existing channel models, the proposed model represents more accurate experimental datasets. We also propose a full error-correction scheme for DNA storage, based on a consensus algorithm and non-binary LDPC codes. Especially, we introduce a novel synchronization method which allows to eliminate remaining deletion errors after the consensus, before applying a belief-propagation LDPC decoding algorithm to correct substitution errors. This method exploits the LDPC code structure to correct deletions, and does not require adding any extra redundancy.
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