Random access in large-scale DNA data storage

@article{Organick2018RandomAI,
  title={Random access in large-scale DNA data storage},
  author={Lee Organick and Siena Dumas Ang and Yuan-Jyue Chen and Randolph Lopez and Sergey Yekhanin and Konstantin Makarychev and Mikl{\'o}s Z. R{\'a}cz and Govinda M. Kamath and Parikshit Gopalan and Bichlien H. Nguyen and Christopher N. Takahashi and Sharon Newman and Hsing-Yeh Parker and Cyrus Rashtchian and Kendall Stewart and Gagan Gupta and Robert Carlson and John Mulligan and Douglas M. Carmean and Georg Seelig and Luis Ceze and Karin Strauss},
  journal={Nature Biotechnology},
  year={2018},
  volume={36},
  pages={242-248}
}
Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted. Here, we encode and store 35 distinct files (over 200 MB of data), in more than 13 million DNA oligonucleotides, and show that we can recover each file individually and with no errors, using a random access… Expand
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