A Guide to Signal Processing Algorithms for Nanopore Sensors

Abstract

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Real-Time Fast Amyloid Seeding and Translocation of α-Synuclein with a Nanopipette

“… 12 − 14 On the other hand, nanopipettes have an adjustable pore diameter at the tip side 10 allowing for label-free detection of protein aggregates and amyloid fibrils 15 by the resistive pulse technique (RPS). 16 , 17 Briefly, the RPS consists of measuring the ionic current perturbation induced by the translocation of an object through the nanopore under a constant voltage. Three parameters are extracted from these current perturbations ( Figure 1 b): (i) the relative current blockade ( ΔI/I 0 ) that depends on the size, the shape, and the conformations of the objects; (ii) the dwell time ( Δt ) that depends on the net charge, the diffusion coefficient, and the interaction of the object with the nanopore inner wall; and (iii) the capture rate ( f ) that is relative to the concentration and the diffusion coefficient of the object.…”

Pushing the limits of nanopore transport performance by polymer functionalization

“…Examples reach from the identification of amino acids based on their transport through a MoS2 nanopore, 209 to support sensing of viruses 210 as well as biomolecules in conical ion track etched pores 211 or DNA base modifications in so-called oxford nanopore sequencing data, 212 and machine learning is in general investigated for nanopore ionic current blockage classification. 213–215…”

“…Examples reach from the identification of amino acids based on their transport through a MoS2 nanopore, 209 to support sensing of viruses 210 as well as biomolecules in conical ion track etched pores 211 or DNA base modifications in so-called oxford nanopore sequencing data, 212 and machine learning is in general investigated for nanopore ionic current blockage classification. [213][214][215] When considering all relevant parameters, their interplay and their dynamics a complete picture of the nanopore transport mechanism will enable improvement of existing nanoporous material applications as well as the development of new technologies. For example, controlling the dynamics of nanopore transport to design a nanopore containing out of equilibrium systems is expected to open avenues towards new technologies.…”

A Critical Review on the Sensing, Control, and Manipulation of Single Molecules on Optofluidic Devices

“…Analyte molecules are introduced on one side of the nanopore, and an application of voltage with proper polarity and magnitude drives the analytes from one side (cis) of the nanopore to the other side (trans) through the nanopore, and this event is usually referred to as translocation. As the analytes pass through the nanopore, they transiently modulate the current, creating a momentary spike in the current signal which is considered a signature of analyte detection [374][375][376]. Usually, the nanopores are sensitive enough to resolve the physical size, structure, charge, and dynamic interaction of analyte molecules which are encoded within the detection signature; therefore, the detection spikes contain a wealth of crucial information about the analyte molecules which can further be extracted upon proper analysis [374,377].…”

“…As the analytes pass through the nanopore, they transiently modulate the current, creating a momentary spike in the current signal which is considered a signature of analyte detection [374][375][376]. Usually, the nanopores are sensitive enough to resolve the physical size, structure, charge, and dynamic interaction of analyte molecules which are encoded within the detection signature; therefore, the detection spikes contain a wealth of crucial information about the analyte molecules which can further be extracted upon proper analysis [374,377]. The working principle of the nanopore is illustrated in Figure 8a, with a typical current trace and detection spike [14].…”