Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA.
Microsoft Research, Redmond, Washington, USA.
Nat Biotechnol. 2018 Mar;36(3):242-248. doi: 10.1038/nbt.4079. Epub 2018 Feb 19.
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.
合成 DNA 具有耐用性，并且可以高密度地编码数字数据，因此它是一种有吸引力的数据存储介质。然而，目前在大规模上恢复存储的数据需要对池中的所有 DNA 进行测序，即使只需要提取信息的一部分。在这里，我们使用随机访问方法，在超过 1300 万个 DNA 寡核苷酸中编码和存储 35 个不同的文件（超过 200MB 的数据），并表明我们可以单独且无误地恢复每个文件。我们设计并验证了一个大型引物库，该库可以使用单个引物来恢复 DNA 中存储的所有文件。我们还开发了一种算法，该算法通过最大化所有序列读取的信息，大大减少了无错误解码所需的测序读取覆盖率。这些进展证明了一种可行的、大规模的 DNA 数据存储和检索系统。
