中文摘要：

单分子荧光共振能量转移（smFRET）技术是当今单分子生物物理研究领域的重要实验手段，该技术通过测量供体、受体荧光光强以及二者间的共振能量转移效率，揭示标记位点间的距离，用于研究DNA、蛋白质等生物大分子的构象变化．然而，当前传统数据处理方法大量依赖人工干预，噪音大，严重影响了实验效率和数据的可靠性．本文提出了一种针对smFRET数据的自动分析算法．该算法主要包括三个部分：基于计算供体与受体荧光光强的相关系数来确定受体与供体对应荧光点的自动匹配算法、甄别错误点的筛选算法以及基于隐马尔可夫模型的全局拟合算法．经改进后的算法大大简化了传统算法中需要人工干预的步骤，而且自动筛除了实验数据中主要的几类噪音．将改进的算法应用于人类端粒重复序列G-四联体fG4）DNA折叠动力学的数据分析，结果显示优化算法比传统算法能够更快地得到更高信噪比的数据，而且该数据结果清晰地表明G4的折叠体现出多态性并受到钾离子浓度的影响．

英文摘要：

The single-molecule fluorescence resonance energy transfer （smFRET） technique plays an important role in the development of biophysics. Measuring the changes of the fluorescence intensities of donor and acceptor and of the FRET efficiency can reveal the changes of distance between the labeling positions. The smFRET may be used to study conformational changes of DNA, proteins and other biomolecules. Traditional algorithm for smFRET data processing is highly dependent on manual operation, leading to high noise, low efficiency and low reliability of the outputs. In the present work, we propose an automatic and more accurate algorithm for smFRET data processing. It consists of three parts： algorithm for automatic pairing of donor and acceptor fluorescence spots based on negative correlation between their intensities; algorithm for data screening by eliminating invalid fluorescence spots sections; algorithm for global data fitting based on Baum-Welch algorithm of hidden Markov model. Based on the law of energy conservation, the light intensity of one pair of donor and acceptor shows a negative correlation. We can use this feature to find the active smFRET pairs automatically. The algorithm will first find out three active smFRET pairs with correlation coefficient lower than the threshold we set. This three active smFRET pairs will provide enough coordinate data for the algorithm to calculate the pairing matrix in the rest of automatic pairing work. After obtaining all the smFRET pairs, the algorithm for data screening will check the correlation coefficient for each pair. The invalid pairs with correlation coefficient higher than the threshold value will be eliminated. The rest of smFRET pairs will be analyzed by the data fitting algorithm. The Baum-Welch algorithm can be used for learning the global parameters. The global parameters we obtained will then be used to fit each FRET-time curve with Viterbi algorithm. The global parameter learning part will help us find the specific FRET efficiency for each state an