PT  - JOURNAL ARTICLE
AU  - Bee, Callista
AU  - Chen, Yuan-Jyue
AU  - Ward, David
AU  - Liu, Xiaomeng
AU  - Seelig, Georg
AU  - Strauss, Karin
AU  - Ceze, Luis
TI  - Content-Based Similarity Search in Large-Scale DNA Data Storage Systems
AID  - 10.1101/2020.05.25.115477
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.25.115477
4099  - http://biorxiv.org/content/early/2020/05/27/2020.05.25.115477.short
4100  - http://biorxiv.org/content/early/2020/05/27/2020.05.25.115477.full
AB  - Synthetic DNA has the potential to store the world’s continuously growing amount of data in an extremely dense and durable medium. Current proposals for DNA-based digital storage systems include the ability to retrieve individual files by their unique identifier, but not by their content. Here, we demonstrate content-based retrieval from a DNA database by learning a mapping from images to DNA sequences such that an encoded query image will retrieve visually similar images from the database via DNA hybridization. We encoded and synthesized a database of 1.6 million images and queried it with a variety of images, showing that each query retrieves a sample of the database containing visually similar images are retrieved at a rate much greater than chance. We compare our results with several algorithms for similarity search in electronic systems, and demonstrate that our molecular approach is competitive with state-of-the-art electronics.One Sentence Summary Learned encodings enable content-based image similarity search from a database of 1.6 million images encoded in synthetic DNA.Competing Interest StatementC.B., Y.C., G.S, K.S, and L.C. have filed a patent application on the core idea. K.S. and Y.C. are employed by Microsoft.