Scaling the High-Yield Potential of Large-Scale DNA Data Storage with Cap-Free DNA Synthesis.

IF 3.9 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
ACS Synthetic Biology Pub Date : 2025-07-18 Epub Date: 2025-07-04 DOI:10.1021/acssynbio.5c00175
Weiming Lin, Haotian Yu, Weihao Li, Yemin Han, Manman Lv, Han Gao, Mengqing Cheng, Yan Huang, Kun Bi, Zuhong Lu, Quanjun Liu
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引用次数: 0

Abstract

DNA has emerged as a promising storage medium for addressing exponentially growing data storage demands, owing to its exceptional information density and chemical stability. However, current DNA synthesis techniques face significant limitations in achieving high-throughput data storage due to constrained synthesis yields. This study presents a novel high-yield cap-free synthesis (HYCFS) strategy that overcomes the limitations of conventional solid-phase phosphoramidite chemistry in both synthesis length and production yield. We established a theoretical product prediction model based on cap-free synthesis characteristics and systematically evaluated the strategy through standard DNA storage workflows. Under column-based synthesis conditions with high coupling efficiency (>99%), this approach demonstrated a 3-fold enhancement in effective sequence yield compared to traditional methods. Theoretical modeling predicts superior performance in array-based synthesis systems for large-scale data storage applications. HYCFS shows potential to enhance DNA-based data storage capacity by 2 orders of magnitude while reducing storage costs, thereby advancing the development of large-scale DNA data storage technologies.

利用无帽DNA合成扩大大规模DNA数据存储的高产潜力。
DNA由于其特殊的信息密度和化学稳定性,已成为解决指数级增长的数据存储需求的有前途的存储介质。然而,目前的DNA合成技术由于合成产量的限制,在实现高通量数据存储方面面临着显著的限制。本研究提出了一种新的高收率无帽合成(HYCFS)策略,克服了传统固相磷酰胺化学在合成长度和产量方面的局限性。我们建立了基于无帽合成特性的理论产品预测模型,并通过标准DNA存储工作流程系统地评估了该策略。在具有高耦合效率(>99%)的柱基合成条件下,该方法的有效序列产率比传统方法提高了3倍。理论建模预测了基于阵列的合成系统在大规模数据存储应用中的优越性能。HYCFS显示出将基于DNA的数据存储容量提高2个数量级的潜力,同时降低存储成本,从而推动大规模DNA数据存储技术的发展。
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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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