【摘要】 在DNA荧光测序中,噪声会影响分析的准确度和检出限。相比其他滤波方法,小波分析具有良好的时频域分辨特性。在小波去噪处理中,正确选择合适的小波基函数、去噪阈值和分解层数直接关系到信号去噪处理的质量。为了真实构建噪声模型并准确评价去噪算法的有效性,实验中通过实际系统中采集到的噪声信号叠加理想荧光信号构建DNA测序仿真信号,去噪分析的结果表明:选择sym7小波基函数、分解层数(lev=5)与使用固定格式软阈值,有效去除了DNA测序信号的噪声;处理后,信号的信噪比提高了5倍以上。将其用于处理实际的DNA电泳荧光信号,相比基于随机噪声模型的算法,去噪后的信号更加真实可靠。更多还原

【Abstract】 Fluorescence signals in DNA sequencing are often contaminated by noise,which has negative influence on the accuracy and detection limit of analysis.Wavelet analysis has excellent time and frequency domain resolution for signal denoising compared to other conventional filtering methods.Before the signal denoising process,a key problem is how to choose a suitable wavelet base,decomposition level and denoising threshold,which have great influence on the quality of signal denoising.In order to construct the same noise model as that in experiment and evaluate the denoising algorithm precisely,a novel method is presented: the real noise signal acquired from the experimental system was added to an ideal signal to simulate a noisy DNA sequencing signal,thus the denoising efficiency could be evaluated accurately.The denoising results indicate that using sym 7 wavelet base,decomposition level at 5 and using fixed form soft threshold can effectively reduce the noise.After being processed,the SNR was improved more than 5 times.When the same algorithm was applied to the experimental DNA sequencing data,the results were more credible than those obtained through other algorithms based on the random noise model.更多还原