Dataset Information

Demonstration of End-to-End Automation of DNA Data Storage.

ABSTRACT: Synthetic DNA has emerged as a novel substrate to encode computer data with the potential to be orders of magnitude denser than contemporary cutting edge techniques. However, even with the help of automated synthesis and sequencing devices, many intermediate steps still require expert laboratory technicians to execute. We have developed an automated end-to-end DNA data storage device to explore the challenges of automation within the constraints of this unique application. Our device encodes data into a DNA sequence, which is then written to a DNA oligonucleotide using a custom DNA synthesizer, pooled for liquid storage, and read using a nanopore sequencer and a novel, minimal preparation protocol. We demonstrate an automated 5-byte write, store, and read cycle with a modular design enabling expansion as new technology becomes available.

SUBMITTER: Takahashi CN

PROVIDER: S-EPMC6428863 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Demonstration of End-to-End Automation of DNA Data Storage.

Takahashi Christopher N CN Nguyen Bichlien H BH Strauss Karin K Ceze Luis L

Scientific reports 20190321 1

Synthetic DNA has emerged as a novel substrate to encode computer data with the potential to be orders of magnitude denser than contemporary cutting edge techniques. However, even with the help of automated synthesis and sequencing devices, many intermediate steps still require expert laboratory technicians to execute. We have developed an automated end-to-end DNA data storage device to explore the challenges of automation within the constraints of this unique application. Our device encodes dat ...[more]

PMID: 30899031

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Project description:Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage solutions. Recent studies suggest leveraging the inherent redundancy of synthesis and sequencing technologies by using composite DNA alphabets. A major challenge of this approach involves the noisy inference process, obstructing large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering, in some implementations, a 6.5-fold increase in logical density over standard DNA-based storage systems, with near-zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter consists of a subset of shortmers. We formally define various combinatorial encoding schemes and investigate their theoretical properties. These include information density and reconstruction probabilities, as well as required synthesis and sequencing multiplicities. We then propose an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional (2D) error correction codes, and reconstruction algorithms, under different error regimes. We performed simulations and show, for example, that the use of 2D Reed-Solomon error correction has significantly improved reconstruction rates. We validated our approach by constructing two combinatorial sequences using Gibson assembly, imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage and describes some theoretical research questions and technical challenges. Combining combinatorial principles with error-correcting strategies, and investing in the development of DNA synthesis technologies that efficiently support combinatorial synthesis, can pave the way to efficient, error-resilient DNA-based storage solutions.

Dataset Information

Demonstration of End-to-End Automation of DNA Data Storage.

Publications

Demonstration of End-to-End Automation of DNA Data Storage.

Similar Datasets

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