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

Abstract

Solid-state nanopores represent a powerful platform for the detection and characterization of a wide range of biomolecules and particles, including proteins, viruses, and nanoparticles, for clinical and biochemical applications. Typically, nanopores operate by measuring transient pulses of ionic current during translocation events of molecules passing through the pore. Given the strong noise and stochastic fluctuations in ionic current recordings during nanopore experiments, signal processing based on the statistical analysis of numerous translocation events remains a crucial issue for nanopore sensing. Based on parallel computational processing and efficient memory management, we 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. By using an adaptive threshold within a sliding window, we could correct the baseline determination process in real time. As a result, the features of translocation event signals could be identified more accurately, especially for the intermittent occurrence of high-density complex signals. The program also demonstrated good signal differentiation. As a ready-to-use software, the data program is more efficient and compatible with diverse nanopore signals, making it suitable for more complex nanopore applications.