A dual-rule encoding DNA storage system using chaotic mapping to control GC content
Abstract Motivation DNA as a novel storage medium is considered an effective solution to the world’s growing demand for information due to its high density and long-lasting reliability. However, early coding schemes ignored the biologically constrained nature of DNA sequences in pursuit of high dens...
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| Published in: | Bioinformatics (Oxford, England) Vol. 40; no. 3 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
04-03-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Abstract
Motivation
DNA as a novel storage medium is considered an effective solution to the world’s growing demand for information due to its high density and long-lasting reliability. However, early coding schemes ignored the biologically constrained nature of DNA sequences in pursuit of high density, leading to DNA synthesis and sequencing difficulties. This article proposes a novel DNA storage coding scheme. The system encodes half of the binary data using each of the two GC-content complementary encoding rules to obtain a DNA sequence.
Results
After simulating the encoding of representative document and image file formats, a DNA sequence strictly conforming to biological constraints was obtained, reaching a coding potential of 1.66 bit/nt. In the decoding process, a mechanism to prevent error propagation was introduced. The simulation results demonstrate that by adding Reed-Solomon code, 90% of the data can still be recovered after introducing a 2% error, proving that the proposed DNA storage scheme has high robustness and reliability.
Availability and implementation: The source code for the codec scheme of this paper is available at https://github.com/Mooreniah/DNA-dual-rule-rotary-encoding-storage-system-DRRC. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1367-4811 1367-4803 1367-4811 |
| DOI: | 10.1093/bioinformatics/btae113 |