Evolutionary approach to construct robust codes for DNA-based data storage

Frontiers in Genetics

Genetics

Junbiao Dai¹  Xiaoluo Huang¹  Qingshan Jiang¹  Qiang Qu¹  Yang Wang¹  Abdur Rasool² 

[1] Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China;

关键词: biocomputing;    DNA coding sets;    bioconstrained codes;    MFO;    DNA data storage;   

DOI  :  10.3389/fgene.2023.1158337

 received in 2023-02-07, accepted in 2023-03-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors.

【 授权许可】

Unknown   
Copyright © 2023 Rasool, Jiang, Wang, Huang, Qu and Dai.