Design considerations for advancing data storage with synthetic DNA for long-term archiving

Ezekannagha, Chisom; Becker, Anke; Heider, Dominik; Hattab, Georges

doi:10.1016/j.mtbio.2022.100306

Cited by 17 publications

(8 citation statements)

References 108 publications

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“…Furthermore, information theory measures, as well as distance measures, were presented as evaluation metrics. These evaluation metrics provide sufficient knowledge and techniques to identify and compare several coding systems [28] . However, most of the attention was directed to error correction and how the DNA channel should be modeled.…”

Section: Related Workmentioning

confidence: 99%

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

Ezekannagha

Welzel

Heider

et al. 2023

Computational and Structural Biotechnology Journal

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Section: Related Workmentioning

confidence: 99%

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

Ezekannagha

Welzel

Heider

et al. 2023

Computational and Structural Biotechnology Journal

Self Cite

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“…Generally, the design of a DNA storage system must meet basic requirements to be less error-prone, reliable and robust. Consider this, thorough overviews [17]- [19] have shared insight into general processes, design considerations, challenges and prospects for sustainable DNA storage. Specifically, figuring out how to store and organize the RDF graph data into DNA enabling query processing inexpensively becomes a challenging problem for database designers.…”

Section: Dna Storage Backgroundmentioning

confidence: 99%

DNA-Based Storage of RDF Graph Data: A Futuristic Approach to Data Analytics

Usmani,

Wiese

2023

IEEE Access

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Future data analytics will require enormous storage space for data-driven decisions, necessitating alternative storage sources for massive data archives. Storage solutions have always been in demand due to the limitations of existing media. Deoxyribonucleic Acid (DNA) is an emergent storage medium suitable for archival storage of rapidly increasing digital volumes. Due to its longevity, DNA storage technology has led to numerous applications to store and retrieve entire data. In this way, DNA synthesis and sequencing costs can be reduced by compressing data in full before it is stored. However, prior works have not used DNA storage to retrieve partial data from complex graphs, while taking advantage of cost-effective advanced analytics. In this paper, we present an efficient DNA-based query processing system to retrieve partial information using RDF graph data. Moreover, using binary search, we fetch and decode significantly fewer DNA strands to obtain partial information about RDF graph data based on SPARQL queries. Specifically, the experimental analysis shows that the average data retrieval per query as output is found less than 1% for RDF graphs with more than 1MB (Megabytes) in size, which consequently reduces a significant amount of sequencing costs.INDEX TERMS Data retrieval, DNA storage, RDF graph data model, SPARQL query processing. I. INTRODUCTION

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“…Therefore, to improve the success rate of data decoding and reading accuracy, researchers must introduce error correction schemes. , Currently, two main types of error correction approaches exist. The first type involves performing logical operations on the original data and adding error correction codes such as Reed-Solomon (RS) and low-density parity-check (LDPC) at the end. − In 2015, Grass et al innovatively applied RS to DNA sequences, utilizing the logical redundancy of the data to achieve error detection and correction within sequences. Shufang and Kang employed LDPC codes for data encoding, combining the characteristics of the encoded data with RS error correction codes to achieve data correction capabilities.…”

Section: Introductionmentioning

confidence: 99%

Storing Images in DNA via base128 Encoding

Wang,

Cao,

et al. 2024

J. Chem. Inf. Model.

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Current DNA storage schemes lack flexibility and consistency in processing highly redundant and correlated image data, resulting in low sequence stability and image reconstruction rates. Therefore, according to the characteristics of image storage, this paper proposes storing images in DNA via base128 encoding (DNA-base128). In the data writing stage, data segmentation and probability statistics are carried out, and then, the data block frequency and constraint encoding set are associated with achieving encoding. When the image needs to be recovered, DNA-base128 completes internal error correction by threshold setting and drift comparison. Compared with representative work, the DNA-base128 encoding results show that the undesired motifs were reduced by 71.2−90.7% and that the local guanine-cytosine content variance was reduced by 3 times, indicating that DNA-base128 can store images more stably. In addition, the structural similarity index (SSIM) and multiscale structural similarity (MS-SSIM) of image reconstruction using DNA-base128 were improved by 19−102 and 6.6−20.3%, respectively. In summary, DNA-base128 provides image encoding with internal error correction and provides a potential solution for DNA image storage. The data and code are available at the GitHub repository: https://github.com/ 123456wk/DNA_base128.

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Design considerations for advancing data storage with synthetic DNA for long-term archiving

Cited by 17 publications

References 108 publications

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

DNA-Based Storage of RDF Graph Data: A Futuristic Approach to Data Analytics

Storing Images in DNA via base128 Encoding

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