Nanopore Data Analysis: Baseline Construction and Abrupt Change-Based Multilevel Fitting
Nanopore Data Analysis: Baseline Construction and Abrupt Change-Based Multilevel Fitting
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DOI：
10.1021/acs.analchem.1c01646
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发表时间：
2021-08-17
影响因子：
7.4
通讯作者：
Kim, Min Jun
中科院分区：
文献类型：
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作者：
Bandara, Y. M. Nuwan D. Y.;Saharia, Jugal;Kim, Min Jun
固态纳米孔技术可提供单分子分辨率信息，而交付成果的质量取决于分析平台提取最大可能事件并对其进行适当拟合的能力。在这项工作中，我们提出了一个分析平台，具有四种基线拟合方法，适用于各种纳米孔痕迹（包括那些在现有平台失败的情况下发生阶跃或突然变化的痕迹），以最大限度地提高可提取事件（在某些情况下提高 2 倍）和多级事件拟合能力。基线拟合方法按照鲁棒性和计算成本递增的顺序包括算术平均、线性拟合、高斯平滑以及高斯平滑和回归混合。使用超稳定到剧烈波动的电流曲线对性能进行了测试，对于剧烈波动的电流曲线，事件计数随着拟合鲁棒性的增加而显着增加。使用 dbscan 方法对事件的转折点进行聚类，然后根据信号级别的突变将其分割为初步级别，然后迭代细化以推断事件的最终级别。最后，我们展示了聚类在多级 DNA 数据分析中的实用性，然后是蛋白质易位谱的评估。
Solid-state nanopore technology delivers single-molecule resolution information, and the quality of the deliverables hinges on the capability of the analysis platform to extract maximum possible events and fit them appropriately. In this work, we present an analysis platform with four baseline fitting methods adaptive to a wide range of nanopore traces (including those with a step or abrupt changes where pre-existing platforms fail) to maximize extractable events (2x improvement in some cases) and multilevel event fitting capability. The baseline fitting methods, in the increasing order of robustness and computational cost, include arithmetic mean, linear fit, Gaussian smoothing, and Gaussian smoothing and regressed mixing. The performance was tested with ultra-stable to vigorously fluctuating current profiles, and the event count increased with increasing fitting robustness prominently for vigorously fluctuating profiles. Turning points of events were clustered using the dbscan method, followed by segmentation into preliminary levels based on abrupt changes in the signal level, which were then iteratively refined to deduce the final levels of the event. Finally, we show the utility of clustering for multilevel DNA data analysis, followed by the assessment of protein translocation profiles.