School of Microelectronics, Tianjin University, Tianjin 300072, China.
Frontier Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2020 Dec 17;20(24):7244. doi: 10.3390/s20247244.
Oxford Nanopore sequencing is an important sequencing technology, which reads the nucleotide sequence by detecting the electrical current signal changes when DNA molecule is forced to pass through a biological nanopore. The research on signal simulation of nanopore sequencing is highly desirable for method developments of nanopore sequencing applications. To improve the simulation accuracy, we propose a novel signal simulation method based on Bi-directional Gated Recurrent Units (BiGRU). In this method, the signal processing model based on BiGRU is built to replace the traditional low-pass filter to post-process the ground-truth signal calculated by the input nucleotide sequence and nanopore sequencing pore model. Gaussian noise is then added to the filtered signal to generate the final simulated signal. This method can accurately model the relation between ground-truth signal and real-world sequencing signal through experimental sequencing data. The simulation results reveal that the proposed method utilizing the powerful learning ability of the neural network can generate the simulated signal that is closer to the real-world sequencing signal in the time and frequency domains than the existing simulation method.
牛津纳米孔测序是一种重要的测序技术，它通过检测 DNA 分子被迫穿过生物纳米孔时的电流信号变化来读取核苷酸序列。对于纳米孔测序应用方法的发展，纳米孔测序信号模拟的研究是非常可取的。为了提高模拟精度，我们提出了一种基于双向门控循环单元（BiGRU）的新型信号模拟方法。在该方法中，基于 BiGRU 的信号处理模型被构建来替代传统的低通滤波器，以对基于输入核苷酸序列和纳米孔测序孔模型计算得到的真实信号进行后处理。然后，向滤波后的信号添加高斯噪声以生成最终的模拟信号。该方法可以通过实验测序数据准确地模拟真实信号与原始信号之间的关系。模拟结果表明，与现有模拟方法相比，利用神经网络强大学习能力的所提出的方法可以在时间和频率域中生成更接近真实世界测序信号的模拟信号。
