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Register a new userSingle-Read Reconstruction for DNA Data Storage Using Transformers
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Added by
Eyar Ben-Tolila
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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As the global need for large-scale data storage is rising exponentially, existing storage technologies are approaching their theoretical and functional limits in terms of density and energy consumption, making DNA based storage a potential solution for the future of data storage. Several studies introduced DNA based storage systems with high information density (petabytes/gram). However, DNA synthesis and sequencing technologies yield erroneous outputs. Algorithmic approaches for correcting these errors depend on reading multiple copies of each sequence and result in excessive reading costs. The unprecedented success of Transformers as a deep learning architecture for language modeling has led to its repurposing for solving a variety of tasks across various domains. In this work, we propose a novel approach for single-read reconstruction using an encoder-decoder Transformer architecture for DNA based data storage. We address the error correction process as a self-supervised sequence-to-sequence task and use synthetic noise injection to train the model using only the decoded reads. Our approach exploits the inherent redundancy of each decoded file to learn its underlying structure. To demonstrate our proposed approach, we encode text, image and code-script files to DNA, produce errors with high-fidelity error simulator, and reconstruct the original files from the noisy reads. Our model achieves lower error rates when reconstructing the original data from a single read of each DNA strand compared to state-of-the-art algorithms using 2-3 copies. This is the first demonstration of using deep learning models for single-read reconstruction in DNA based storage which allows for the reduction of the overall cost of the process. We show that this approach is applicable for various domains and can be generalized to new domains as well.
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With the rapid increase of available digital data, DNA storage is identified as a storage media with high density and capability of long-term preservation, especially for archival storage systems. However, the encoding density (i.e., how many binary bits can be encoded into one nucleotide) and error handling are two major factors intertwined in DNA storage. Considering encoding density, theoretically, one nucleotide can encode two binary bits (upper bound). However, due to biochemical constraints and other necessary information associated with payload, the encoding densities of various DNA storage systems are much less than this upper bound. Additionally, all existing studies of DNA encoding schemes are based on static analysis and really lack the awareness of dynamically changed digital patterns. Therefore, the gap between the static encoding and dynamic binary patterns prevents achieving a higher encoding density for DNA storage systems. In this paper, we propose a new Digital Pattern-Aware DNA storage system, called DP-DNA, which can efficiently store digital data in DNA storage with high encoding density. DP-DNA maintains a set of encoding codes and uses a digital pattern-aware code (DPAC) to analyze the patterns of a binary sequence for a DNA strand and selects an appropriate code for encoding the binary sequence to achieve a high encoding density. An additional encoding field is added to the DNA encoding format, which can distinguish the encoding scheme used for those DNA strands, and thus we can decode DNA data back to its original digital data. Moreover, to further improve the encoding density, a variable-length scheme is proposed to increase the feasibility of the coding scheme with a high encoding density. Finally, the experimental results indicate that the proposed DP-DNA achieves up to 103.5% higher encoding densities than prior work.
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