CRISPR-powered quantitative keyword search engine in DNA data storage

Jiongyu Zhang, Chengyu Hou and Changchun Liu ()
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Jiongyu Zhang: University of Connecticut Health Center
Chengyu Hou: University of Connecticut Health Center
Changchun Liu: University of Connecticut Health Center

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

Date: 2024
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DOI: 10.1038/s41467-024-46767-x

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