Reconstruction of encoded data in DNA storage technology - Subhasiny, Sankar (Nanyang Technological University, 2022)
Inevitable biomolecular errors in DNA storage technology could be resolved by designing robust error correction codes or intelligent clustering/decoding algorithms. The first objective of our work is to reconstruct the encoded DNA sequences read from the Illumina sequencer before decoding by studying the efficiencies of the existing clustering tools in the biological domain and then modifying, tuning, and analyzing their applicability in the DNA data storage domain. The investigated tools and algorithms include Starcode, Cooperative Sequence Clustering, Majority nucleotide selection algorithm, Slidesort, and MeShClust. We observed and compared them, Starcode, Majority nucleotide selection algorithm and Cooperative Sequence Clustering yields the highest recovery rate with less sequencing redundancy for three datasets. The benefit of portability using nanopore-based storage leads to the second objective of designing a Nanopore based DNA storage simulator that can serve as a tool for evaluating coding and clustering techniques. We simulated the DNA channel and subsampling of sequenced data using the non-parametric subsampling method by studying the distribution of real nanopore DNA storage data and then integrated it with DeepSimulator. The design is evaluated for its accuracy by comparing it with real nanopore reads. Besides, nanopore reads obtained from the designed simulator are clustered and representatives in each cluster are extracted for reconstructing the encoded data.