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Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.

Dario Dematties1, Chenyu Wen2, Mauricio David Pérez2

  • 1Instituto de Ciencias Humanas, Sociales y Ambientales CONICET Mendoza Technological Scientific Center, Mendoza M5500, Argentina.

ACS Nano
|September 29, 2021
PubMed
Summary

A novel deep learning approach, the bi-path network (B-Net), objectively extracts features from nanopore sensor data. This method accurately identifies biological molecule translocations even in noisy signals, outperforming traditional algorithms.

Keywords:
deep learningfeature extractionnanopore sensorsneural networkpulse-like signals

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Area of Science:

  • Biophysics
  • Nanotechnology
  • Machine Learning

Background:

  • Nanopore sensors detect analytes via electrical resistance changes, generating pulse-like signals.
  • Current feature extraction methods rely on subjective, user-defined thresholds, limiting objectivity and performance in noisy data.
  • Existing algorithms struggle with low signal-to-noise ratios, hindering accurate analysis of translocating molecules.

Purpose of the Study:

  • To develop an objective, data-driven method for feature extraction from nanopore sensor current traces.
  • To overcome limitations of traditional threshold-based algorithms in analyzing noisy pulse-like signals.
  • To introduce a versatile deep learning architecture for analyzing biological molecule translocation events.

Main Methods:

  • Implementation of a bi-path network (B-Net) deep learning architecture for pulse recognition and feature extraction.
  • Training the B-Net on simulated and experimental datasets of DNA and protein translocation.
  • Evaluation of B-Net performance against traditional threshold-based methods, particularly in low signal-to-noise conditions.

Main Results:

  • The B-Net successfully extracts features and recognizes pulses without requiring predefined parameters.
  • Results demonstrate small relative errors and stable trends in both simulated and experimental data.
  • The B-Net effectively processes data with a signal-to-noise ratio of 1, a feat impossible for threshold-based algorithms.

Conclusions:

  • The bi-path network (B-Net) offers an objective and robust solution for feature extraction in nanopore sensing.
  • This deep learning approach significantly enhances the analysis of biological molecule translocation events, especially in challenging noisy environments.
  • The B-Net architecture is generalizable and applicable to various pulse-like signal analysis tasks beyond nanopore sensing.