Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design
محفوظ في:
| الحاوية / القاعدة: | Nature Communications vol. 16, no. 1 (2025), p. 6388 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , , , , , , , , , , |
| منشور في: |
Nature Publishing Group
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Imburgia, Carina |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 245 | 1 | |a Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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 images into a reduced-dimensional embedding, encoding each embedding as a Cas9 target sequence. These target sequences act as molecular addresses, capturing clusters of semantically related images. By leveraging Cas9’s off-target cleavage activity, query sequences cleave both exact and closely related targets, enabling high-fidelity retrieval of molecular addresses corresponding to in silico image clusters similar to the query. These approaches move towards addressing key challenges in molecular data retrieval by offering simplified, rapid isothermal protocols and new DNA data access capabilities.CRISPR-Cas9 has potential as an efficient tool for information retrieval in DNA data storage. Here the authors present a Cas9-based random access and similarity search approach and test on DNA databases, progressing toward simpler, isothermal protocols. | |
| 653 | |a Databases | ||
| 653 | |a Similarity | ||
| 653 | |a Metadata | ||
| 653 | |a CRISPR | ||
| 653 | |a Random access | ||
| 653 | |a Information retrieval | ||
| 653 | |a Digital data | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Data storage | ||
| 653 | |a Clusters | ||
| 653 | |a Nucleotide sequence | ||
| 653 | |a Machine learning | ||
| 653 | |a Data retrieval | ||
| 653 | |a Storage systems | ||
| 653 | |a Information storage | ||
| 653 | |a Gene sequencing | ||
| 653 | |a Searching | ||
| 653 | |a Deoxyribonucleic acid--DNA | ||
| 653 | |a Multiplexing | ||
| 653 | |a Design | ||
| 653 | |a Images | ||
| 653 | |a Information processing | ||
| 653 | |a Latency | ||
| 653 | |a Embedding | ||
| 653 | |a Neural networks | ||
| 653 | |a Semantics | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Organick, Lee |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Zhang, Karen |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Cardozo, Nicolas |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a McBride, Jeff |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Bee, Callista |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Wilde, Delaney |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Roote, Gwendolin |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Jorgensen, Sophia |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Ward, David |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Anderson, Charlie |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Strauss, Karin |u Microsoft Research, Redmond, USA (GRID:grid.419815.0) (ISNI:0000 0001 2181 3404) | |
| 700 | 1 | |a Ceze, Luis |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 700 | 1 | |a Nivala, Jeff |u Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); Molecular Engineering and Sciences Institute, University of Washington, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) | |
| 773 | 0 | |t Nature Communications |g vol. 16, no. 1 (2025), p. 6388 | |
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