R&D: Molecular-Level Similarity Search Brings Computing to DNA Storage

Nature Communications has published an article written by Callista Bee, Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA, Yuan-Jyue Chen, Microsoft Research, Redmond, WA, USA, Melissa Queen, David Ward, Xiaomeng Liu, Lee Organick, Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA, Georg Seelig, Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA, and Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA, Karin Strauss, Microsoft Research, Redmond, WA, USA, and Luis Ceze, Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.

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.“