Simulation Study Of The Base Electrical Signal Recognition Algorithm For Carbon Nanopore DNA Sequencing

The DNA sequencing technology plays a very important role in the research of life sciences as well as medicine. Since the birth of generation 1 sequencing technology in the1970's, the DNA sequencing technology has been progressing rapidly. As a generation 3sequencing technology, the carbon nanopore sequencing technology has drawn the attention of researchers for its characteristics such as fast sequencing speed, high sequencing throughput and low cost. The carbon nanopore sequencing technology is the technology most likely to bring the individual whole genome sequencing cost down to below USD$1,000.The electric current generated from the base of different types shows difference when it passes through the carbon nanopore under the action of electrophoresis, which constitutes the theoretical basis of the carbon nanopore sequencing technology. The research group uses the patch clamp amplifier as the signal detection device to detect the base current and uses the USB2.0 interface technology and the FPGA technology to design the data collection system. Based on it, this article has perfected the last step in the sequencing system--- the base signal recognition algorithm and has achieved a complete set of processes from the system's collection of the base signal to data transmission and finally to successful identification of the type of the base.Starting from the features of the patch clamp amplifier, this article discusses the amplifier's possibility of serve as the weak current amplifier for the sequencing system,explores the basic principle of the base perforation generating the electric signal,establishes the model of the signal generating circuit, analyses the properties of the signal and uses the square wave plus the white noise to simulate the base signal. The threshold method is used to make measurement of the two characteristic values of signals--- width and amplitude value and also to make comparison of the results determined in the threshold method after the signal wave is filtered with a traditional wave filter and goes through wavelet denoising. Wavelet conversion is applied to determine the signal boundary and measure the characteristic value of the signal. The clustering analysis method is employed to classify the signals based on the characteristic value thus to classify each signal recognition.In this article, it is found through research that the threshold method has a big error rate without denoising the signal. After the signal is denoised using a traditional wave filter, the threshold method is unable to precisely determine the width of the signal. Under a signal to noise ratio of 15 d B, the threshold method based on wavelet denoising has a good effect on the precise determination of the accuracy, width and amplitude of the signal.Under a signal to noise ratio of 10 d B, the measurement precision in the threshold method based on wavelet denoising greatly declines. Wavelet conversion can precisely determine the signal boundary and shows a high precision under a signal to noise ratio of 10 d B and 2d B. The clustering analysis method is highly capable of identifying the signals according to the characteristic values thereof and classifies the same into four types.