| Literature DB >> 29457795 |
Lee Organick1, Siena Dumas Ang2, Yuan-Jyue Chen2, Randolph Lopez3, Sergey Yekhanin2, Konstantin Makarychev2, Miklos Z Racz2, Govinda Kamath2, Parikshit Gopalan2, Bichlien Nguyen2, Christopher N Takahashi1, Sharon Newman1, Hsing-Yeh Parker2, Cyrus Rashtchian2, Kendall Stewart1, Gagan Gupta2, Robert Carlson2, John Mulligan2, Douglas Carmean2, Georg Seelig1,4, Luis Ceze1, Karin Strauss2.
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.Entities:
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Year: 2018 PMID: 29457795 DOI: 10.1038/nbt.4079
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908