发表机构
School of Mathematics and KL-AAGDM, Tianjin University; Center for Applied Mathematics, Tianjin University（天津大学数学学院; 天津大学应用数学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出高维半参数中介分析框架，以GC含量为暴露、损伤谱为中介，识别DNA存储失效途径，发现14个单碱基缺失中介，集中于C结尾三核苷酸，为优化存储可靠性提供机制导向方法。
AI 中文摘要
DNA数据存储提供了非凡的信息密度和长期耐久性，但其可靠性受到合成过程中引入并在存储期间积累的序列依赖性错误的限制。目前尚不清楚序列组成如何通过特定的分子损伤成分与存储失效相关联。我们开发了一个针对生存结果的高维半参数中介框架。GC含量被视为暴露变量，高维基线损伤谱（按三核苷酸上下文和错误类型分层的每次读取损伤计数的向量）作为中介变量，存储质量失效作为结果变量。中介模型和生存模型中的非线性协变量效应均使用深度神经网络进行近似。我们开发了一个三步程序，结合系数乘积筛选、平滑剪切绝对偏差（SCAD）惩罚估计和联合显著性检验，用于中介变量的选择和推断。应用于电化学合成DNA的老化实验，该方法识别出14个显著的中介变量，全部对应于单碱基缺失，估计的中介效应集中在以C结尾的三核苷酸上下文中。这些结果揭示了缺失型损伤是连接序列组成与档案可靠性降低的主要途径，并为未来的优化和错误控制策略提出了候选序列特征。因此，所提出的框架为理解和提高DNA数据存储的可靠性提供了一种机制导向的统计方法。
英文摘要
DNA data storage offers extraordinary information density and long-term durability, but its reliability is limited by sequence-dependent errors introduced during synthesis and accumulated during storage. It remains unclear how sequence composition is associated with storage failure through specific molecular damage components. We develop a high-dimensional semiparametric mediation framework for survival outcomes. GC content is treated as the exposure, a high-dimensional baseline damage spectrum (a vector of per-read damage counts stratified by trinucleotide context and error type) as the mediator, and storage-quality failure as the outcome. Nonlinear covariate effects in both the mediator and survival models are approximated using deep neural networks. A three-step procedure combining product-of-coefficients screening, Smoothly Clipped Absolute Deviation (SCAD) penalized estimation, and joint significance testing is developed for mediator selection and inference. Applied to an aging experiment on electrochemically synthesized DNA, the method identifies 14 significant mediators, all corresponding to single-base deletions, with estimated mediated effects concentrated in trinucleotide contexts ending in C. These results reveal deletion-type damage as a major pathway linking sequence composition to reduced archival reliability and suggest candidate sequence features for future optimization and error-control strategies. The proposed framework thus offers a mechanism-oriented statistical approach for understanding and improving the reliability of DNA data storage.
