Department of Chemistry, University of Houston, Houston, Texas, USA.
Biophys J. 2010 Jan 6;98(1):164-73. doi: 10.1016/j.bpj.2009.09.047.
A method to denoise single-molecule fluorescence resonance energy (smFRET) trajectories using wavelet detail thresholding and Bayesian inference is presented. Bayesian methods are developed to identify fluorophore photoblinks in the time trajectories. Simulated data are used to quantify the improvement in static and dynamic data analysis. Application of the method to experimental smFRET data shows that it distinguishes photoblinks from large shifts in smFRET efficiency while maintaining the important advantage of an unbiased approach. Known sources of experimental noise are examined and quantified as a means to remove their contributions via soft thresholding of wavelet coefficients. A wavelet decomposition algorithm is described, and thresholds are produced through the knowledge of noise parameters in the discrete-time photon signals. Reconstruction of the signals from thresholded coefficients produces signals that contain noise arising only from unquantifiable parameters. The method is applied to simulated and observed smFRET data, and it is found that the denoised data retain their underlying dynamic properties, but with increased resolution.
本文提出了一种使用小波细节阈值和贝叶斯推断对单分子荧光共振能量（smFRET）轨迹进行去噪的方法。开发了贝叶斯方法来识别时间轨迹中的荧光团光闪烁。使用模拟数据来量化静态和动态数据分析的改进。该方法在实验 smFRET 数据中的应用表明，它可以区分光闪烁和 smFRET 效率的大变化，同时保持无偏方法的重要优势。检查并量化了已知的实验噪声源，作为通过对小波系数进行软阈值处理来去除其贡献的一种手段。描述了一种小波分解算法，并通过离散时间光子信号中噪声参数的知识生成阈值。从阈值系数重建信号会产生仅包含不可量化参数引起的噪声的信号。该方法应用于模拟和观察到的 smFRET 数据，结果表明，去噪后的数据保留了其潜在的动态特性，但分辨率提高了。
