Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering

The rapid growth of digital data, projected to reach 180 zettabytes by 2025, is causing a data storage crisis that cannot be addressed by existing storage technologies [Rydning, 2022]. In response, deoxyribonucleic acid (DNA) is emerging as a promising alternative storage medium due to its incredible density and durability. The DNA storage process includes four stages illustrated in Figure 1: (1) an "encoding" stage in which binary data files are encoded into DNA strands (design files) using error-correcting code (ECC) [Koblitz et al., 2000] schemes that may also overcome errors, (2) a "synthesis" stage, which produces artificial DNA strands of each design strand and are then stored in a storage container [LeProust et al., 2010], (3) a "sequencing" stage [Anavy et al., 2019, Erlich and Zielinski, 2017, Organick et al., 2018, Yazdi et al., 2017] which translates a DNA strand into a digital sequence known as a "read", and (4) a "retrieval" stage where reads are decoded back to binary data files while correcting any errors using the chosen coding methods. Despite the vast potential of DNA storage, current DNA sequencers are yet to overcome challenges such as low throughput and high costs compared to the traditional alternatives [Alliance, 2021, Shomorony et al., 2022, Yazdi et al., 2015]. The emerging Nanopore technology offers real-time sequencing of DNA strands with drastically lower costs and portability compared to traditional Illumina sequencing machines [Jain et al., 2016, Kono and Arakawa, 2019]. Despite having higher error rates compared to other sequencing technologies such as Illumina, Nanopore sequencing is gaining significant attention due to its lower cost, portability, and capability to sequence longer strands of DNA.