Rewritable two-dimensional DNA-based data storage with machine learning reconstruction
| Autor: | Chao Pan, S Kasra Tabatabaei, Olgica Milenkovic, Alvaro G. Hernandez, S. M. Hossein Tabatabaei Yazdi, Charles M. Schroeder |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Inpainting General Physics and Astronomy Information Storage and Retrieval Machine learning computer.software_genre ENCODE General Biochemistry Genetics and Molecular Biology Image (mathematics) Machine Learning chemistry.chemical_compound Encoding (memory) Redundancy (engineering) Gene Library Sequence Metadata Multidisciplinary business.industry Nucleic acid sequence General Chemistry DNA chemistry Computer data storage Artificial intelligence business computer |
| Zdroj: | Nature communications. 13(1) |
| ISSN: | 2041-1723 |
| Popis: | DNA-based data storage platforms traditionally encode information only in the nucleotide sequence of the molecule. Here we report on a two-dimensional molecular data storage system that records information in both the sequence and the backbone structure of DNA and performs nontrivial joint data encoding, decoding and processing. Our 2DDNA method efficiently stores high-density images in synthetic DNA and embeds pertinent metadata as nicks in the DNA backbone. To avoid costly worst-case redundancy for correcting sequencing/rewriting errors and to mitigate issues associated with mismatched decoding parameters, we develop machine learning techniques for automatic discoloration detection and image inpainting. The 2DDNA platform is experimentally tested by reconstructing a library of images with undetectable or small visual degradation after readout processing, and by erasing and rewriting copyright metadata encoded in nicks. Our results demonstrate that DNA can serve both as a write-once and rewritable memory for heterogenous data and that data can be erased in a permanent, privacy-preserving manner. Moreover, the storage system can be made robust to degrading channel qualities while avoiding global error-correction redundancy. |
| Databáze: | OpenAIRE |
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