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A Guide to Signal Processing Algorithms for Nanopore Sensors.

Chenyu Wen1, Dario Dematties2, Shi-Li Zhang1

  • 1Division of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, SE-751 03 Uppsala, Sweden.

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|October 4, 2021
PubMed
Summary

Signal processing is crucial for nanopore sensing to extract analyte information from noisy ionic currents. This guide categorizes machine learning (ML)-based and non-ML-based algorithms, detailing their development and application for enhanced nanopore sensing.

Keywords:
analyte identificationfeature extractionmachine learningnanopore sensingneural networkpulse-like signalsignal processing algorithmspike recognition

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Area of Science:

  • Nanopore technology
  • Biosensing
  • Signal Processing

Background:

  • Nanopore technology offers diverse applications including biomedical sensing and chemical detection.
  • Interactions between translocating analytes and nanopores generate ionic current fluctuations containing valuable information.
  • Signal processing is essential to overcome noise and extract meaningful data from these ionic currents.

Purpose of the Study:

  • To systematically evaluate signal processing algorithms for nanopore sensing.
  • To categorize algorithms into Machine Learning (ML)-based and non-ML-based approaches.
  • To provide a guide for developing and implementing ML-based algorithms for nanopore sensing.

Main Methods:

  • Untangling the signal processing flow in nanopore sensing.
  • Categorizing algorithms based on ML versus non-ML approaches.
  • Evaluating algorithm architectures, properties, development tactics, and implementation examples.

Main Results:

  • A systematic evaluation of ML-based and non-ML-based algorithms for nanopore sensing.
  • Discussion of algorithm features, key issues, and implementation strategies.
  • Guidance on building ML-based algorithms, including learning strategies and data handling.

Conclusions:

  • Signal processing is vital for unlocking the full potential of nanopore sensing.
  • A clear distinction and evaluation of ML-based and non-ML-based algorithms are presented.
  • Strategies for future algorithm development in nanopore sensing are outlined.