Loeff Luuk, Kerssemakers Jacob W J, Joo Chirlmin, Dekker Cees
Kavli Institute of Nanoscience and Department of Bionanoscience, Delft University of Technology, 2629 HZ Delft, The Netherlands.
Patterns (N Y). 2021 Apr 30;2(5):100256. doi: 10.1016/j.patter.2021.100256. eCollection 2021 May 14.
Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, , that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.
单分子技术能够以高时空分辨率可视化核酸和蛋白质的分子动力学。当确定单分子时间轨迹内的离散状态时，可以获得生物分子的有价值的动力学信息。在这里，我们提出了一种快速、自动化且无偏差的步长检测方法，该方法可以在无需事先了解噪声贡献和步长位置的情况下确定大型数据集中的步长。该分析基于一系列分区事件，这些事件可使数据与拟合之间的差异最小化。双程策略可确定最佳拟合，并允许检测各种大小的步长。我们展示了针对各种实验轨迹的步长检测。用户友好的界面和步长的自动检测提供了一个强大的分析程序，使任何没有编程知识的人都能在不到一小时的时间内生成步长拟合和信息丰富的图表。
