A robust signal processing program for nanopore signals by dynamic correction threshold with compatible baseline fluctuations

Abstract

Solid-state nanopores represent a powerful platform for the detection and characterization of a broader scope of biomolecules and particles, including proteins, viruses, and nanoparticles, for clinical and biochemical applications. Typically, nanopores work by measuring transient pulses of the ionic current as translocation events of molecules passing through the pore. In view of the strong noise and stochastic fluctuation of ionic current recording in nanopore experiments, the signal processing based on the statistical analysis of massive translocation events remains a crucial issue for nanopore sensing. Based on parallel computational processing and efficient memory management, we have developed a novel signal processing procedure for translocation events to improve the signal identification performance of solid-state nanopores in the presence of baseline oscillation interference. Obviously, by means of an adaptive threshold in a sliding window, we can correct the baseline determination process in real time. As a result, the features of the translocation event signals can be identified more accurately, especially for the intermittent occurrence of high-density complex signals, and the program also shows good signal differentiation. As a ready-to-use software, the data program is more efficient and compatible with diverse nanopore signals for more complex nanopore applications.