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


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
21 03 2019
Historique:
received: 26 10 2018
accepted: 05 03 2019
entrez: 23 3 2019
pubmed: 23 3 2019
medline: 2 10 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 30899031
doi: 10.1038/s41598-019-41228-8
pii: 10.1038/s41598-019-41228-8
pmc: PMC6428863
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

4998

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Auteurs

Christopher N Takahashi (CN)

School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA. cnt@cs.washington.edu.

Bichlien H Nguyen (BH)

School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.
Microsoft Research, Redmond, Washington, USA.

Karin Strauss (K)

School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.
Microsoft Research, Redmond, Washington, USA.

Luis Ceze (L)

School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.

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Classifications MeSH