Analysis of Nanopore Data: Classification Strategies for an Unbiased Curation of Single-Molecule Events from DNA Nanostructures.
Analysis of Nanopore Data: Classification Strategies for an Unbiased Curation of Single-Molecule Events from DNA Nanostructures.
复制标题
纳米孔数据分析：DNA 纳米结构单分子事件公正管理的分类策略。
DOI：
10.1021/acssensors.3c00751
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发表时间：
2023
期刊：
影响因子：
8.9
通讯作者：
Tabard-Cossa,Vincent
中科院分区：
文献类型：
--
作者：
Roelen,Zachary;Briggs,Kyle;Tabard-Cossa,Vincent
纳米孔是一种多功能的单分子传感器，在分子数据存储和疾病生物标志物检测中被用于检测越来越复杂的结构分子混合物。然而，分子复杂性的增加给纳米孔数据分析带来了额外的挑战，包括更多的易位事件因不匹配预期的信号结构而被拒绝，以及更大的选择偏差风险进入事件管理过程。为了突出这些挑战，在这里，我们展示了一个模型分子系统的分析，该系统由一个纳米结构的DNA分子连接到一个线性DNA载体上。我们利用Nanolyzer（一种为纳米孔事件拟合提供的图形分析工具）在事件分段能力方面的最新进展，并描述了事件子结构分析的方法。在此过程中，我们确定并讨论了在分子系统分析中出现的选择偏差的重要来源，并考虑了分子构象和可变实验条件（例如，孔径）的复杂影响。然后，我们提出了对现有分析技术的额外改进，允许改进多路样品的分离，更少的易位事件被拒绝为假阴性，以及更广泛的实验条件，可以提取准确的分子信息。增加纳米孔数据中分析事件的覆盖范围不仅对高保真度表征复杂分子样品很重要，而且随着机器学习方法用于数据分析和事件识别的不断普及，对于生成准确、无偏见的训练数据也变得至关重要。
Nanopores are versatile single-molecule sensors that are being used to sense increasingly complex mixtures of structured molecules with applications in molecular data storage and disease biomarker detection. However, increased molecular complexity presents additional challenges to the analysis of nanopore data, including more translocation events being rejected for not matching an expected signal structure and a greater risk of selection bias entering this event curation process. To highlight these challenges, here, we present the analysis of a model molecular system consisting of a nanostructured DNA molecule attached to a linear DNA carrier. We make use of recent advances in the event segmentation capabilities of Nanolyzer, a graphical analysis tool provided for nanopore event fitting, and describe approaches to the event substructure analysis. In the process, we identify and discuss important sources of selection bias that emerge in the analysis of this molecular system and consider the complicating effects of molecular conformation and variable experimental conditions (e.g., pore diameter). We then present additional refinements to existing analysis techniques, allowing for improved separation of multiplexed samples, fewer translocation events rejected as false negatives, and a wider range of experimental conditions for which accurate molecular information can be extracted. Increasing the coverage of analyzed events within nanopore data is not only important for characterizing complex molecular samples with high fidelity but is also becoming essential to the generation of accurate, unbiased training data as machine-learning approaches to data analysis and event identification continue to increase in prevalence.