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R&D: CRISPR-Powered Quantitative Keyword Search Engine in DNA Storage

Present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when DNA target corresponding to keyword of interest is present.

Nature Communications has published an article written by Jiongyu Zhang, Chengyu Hou, Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, 06030, USA, and Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA, and Changchun Liu, Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, 06030, USA.

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.

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