Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design.

ABSTRACT: DNA is a promising medium for digital data storage due to its exceptional data density and longevity. Practical DNA-based storage systems require selective data retrieval to minimize decoding time and costs. In this work, we introduce CRISPR-Cas9 as a user-friendly tool for multiplexed, low-latency molecular data extraction. We first present a one-pot, multiplexed random access method in which specific data files are selectively cleaved using a CRISPR-Cas9 addressing system and then sequenced via nanopore technology. This approach was validated on a pool of 1.6 million DNA sequences, comprising 25 unique data files. We then developed a molecular similarity-search approach combining machine learning with Cas9-based retrieval. Using a deep neural network, we mapped a database of 1.74 million

SUBMITTER: Imburgia C 

PROVIDER: S-EPMC12246221 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature