Molecular-level similarity search brings computing to DNA data storage - Bee, Callista; Chen, Yuan-Jyue; Queen, Melissa; Ward, David; Liu, Xiaomeng; Organick, Lee; Seelig, Georg; Strauss, Karin; Ceze, Luis (Nature Communications, 2021)
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 prespeciﬁed 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.