Molecular-level similarity search brings computing to DNA data storage

Callista Bee, Yuan-Jyue Chen, Melissa Queen, David Ward, Xiaomeng Liu, Lee Organick, Georg Seelig, Karin Strauss () and Luis Ceze ()
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Callista Bee: University of Washington
Yuan-Jyue Chen: Microsoft Research
Melissa Queen: University of Washington
David Ward: University of Washington
Xiaomeng Liu: University of Washington
Lee Organick: University of Washington
Georg Seelig: University of Washington
Karin Strauss: Microsoft Research
Luis Ceze: University of Washington

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract As global demand for digital storage capacity grows, storage technologies based on synthetic DNA have emerged as a dense and durable alternative to traditional media. Existing approaches leverage robust error correcting codes and precise molecular mechanisms to reliably retrieve specific files from large databases. Typically, files are retrieved using a pre-specified key, analogous to a filename. However, these approaches lack the ability to perform more complex computations over the stored data, such as similarity search: e.g., finding images that look similar to an image of interest without prior knowledge of their file names. Here we demonstrate a technique for executing similarity search over a DNA-based database of 1.6 million images. Queries are implemented as hybridization probes, and a key step in our approach was to learn an image-to-sequence encoding ensuring that queries preferentially bind to targets representing visually similar images. Experimental results show that our molecular implementation performs comparably to state-of-the-art in silico algorithms for similarity search.

Date: 2021
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DOI: 10.1038/s41467-021-24991-z

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