Random access in large-scale DNA data storage

Lee Organick, Siena Dumas Ang, Yuan Jyue Chen, Randolph Lopez, Sergey Yekhanin, Konstantin Makarychev, Miklos Z. Racz, Govinda Kamath, Parikshit Gopalan, Bichlien Nguyen, Christopher N. Takahashi, Sharon Newman, Hsing Yeh Parker, Cyrus Rashtchian, Kendall Stewart, Gagan Gupta, Robert Carlson, John Mulligan, Douglas Carmean, Georg SeeligLuis Ceze, Karin Strauss

Research output: Contribution to journalArticlepeer-review

451 Scopus citations

Abstract

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 approach. We design and validate a large library of primers that enable individual recovery of all files stored within the DNA. We also develop an algorithm that greatly reduces the sequencing read coverage required for error-free decoding by maximizing information from all sequence reads. These advances demonstrate a viable, large-scale system for DNA data storage and retrieval.

Original languageEnglish (US)
Pages (from-to)242-248
Number of pages7
JournalNature biotechnology
Volume36
Issue number3
DOIs
StatePublished - Mar 1 2018

All Science Journal Classification (ASJC) codes

  • Applied Microbiology and Biotechnology
  • Bioengineering
  • Molecular Medicine
  • Biotechnology
  • Biomedical Engineering

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