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Demonstration of End-to-End Automation of DNA Data Storage.
Open accessPublished version of a preprint
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.
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In the citing paper
“Takahashi et al. (2018) developed a complete end-to-end DNA data storage device.”
From Large-Scale de Novo Oligonucleotide Synthesis for Whole-Genome Synthesis and Data Storage: Challenges and Opportunities (2021) · cites this paper as Takahashi et al., 2018
In the citing paper
“Both chemical and enzymatic syntheses are severely limited by the low speed of these processes.29,39”
From Emerging Approaches to DNA Data Storage: Challenges and Prospects (2022) · cites this paper as [39]
In the citing paper
“Therefore, it is still challenging to develop a DNA-based database of full integration, high efficiency, and practical applicability, even using an automatic fluidic system (30).”
From Electrochemical DNA synthesis and sequencing on a single electrode with scalability for integrated data storage (2021) · cites this paper as [30]
In the citing paper
“Last, the small dead volume of the SlipChip device can save the expensive reagents to make it more cost-effective to write and read a large volume of data compared with conventional systems (30).”
From Electrochemical DNA synthesis and sequencing on a single electrode with scalability for integrated data storage (2021) · cites this paper as [30]
In the citing paper
“Recent work on the automation of a synthesisto-sequencing process has shown, that an automated writeread-cycle is feasible”
From Integrating DNA Encapsulates and Digital Microfluidics for Automated Data Storage in DNA (2022) · cites this paper as [13]
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Citations
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Metadata
- Authors
- Takahashi, C. N., Nguyen, B. H., Strauss, K., & Ceze, L.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Corresponding author
- Takahashi, Christopher N.
- Venue
- Scientific Reports
- Published
- 2019-03-21
- Volume
- 9
- Issue
- 1
- Pages
- 4998-4998
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6428863
- OpenAlex
- W2897609915
- Cited by
- 137
- References
- 17
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 21 · 2023: 27 · 2022: 22 · 2021: 19 · 2020: 16 · 2019: 10
- FWCI
- 5.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Microsoft Research
- United States Department of Defense | Defense Advanced Research Projects Agency (W911-NF-18-2-0034)
- Defense Sciences Office, DARPA (W911-NF-18-2-0034)
- Topics
- Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Data Storage Technologies
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Keywords
- DNA data storage, synthetic DNA, end-to-end automation, nanopore sequencing, data encoding
- MeSH terms
- Automation, DNA, Humans, Information Storage and Retrieval, Sequence Analysis, DNA, Computational Biology
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