Year: 2025
Type: conference-paper
Abstract: Capillary electrophoresis (CE) is an important technology of the first generation of Sanger sequencing. Although the current sequencing technology has gradually developed to the fourth generation, it is still chosen by the majority of users due to its low cost and high precision. It has always been the "gold standard" in the sequencing industry. However, due to noise interference, fluorescence signal crosstalk and other reasons, the DNA sequencing signals collected from the analytical instrument cannot be directly used for sequence recognition or typing analysis. They need to go through a series of processing to remove redundant data and environmental noise in the signal. Due to its good time-frequency characteristics and multi-resolution analysis characteristics, wavelet transform has been successfully used in many fields. This paper proposes an improved wavelet threshold denoising method. This method optimizes the traditional threshold function and threshold to reduce the noise in the signal while maintaining the key features of the signal. Through experimental analysis of simulated signals and real capillary electrophoresis signals, the results show that the improved wavelet threshold denoising method is superior to the traditional hard threshold, soft threshold and Garrote threshold methods in terms of signal-to-noise ratio (SNR), root mean square error (RMSE) and peak signal-to-noise ratio (PSNR).
Authors: Shuoxing Zhu, Jijun Zhu
Institution: Southeast University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Southeast University
OpenAlex ID: W4413008086