QuipuNet: convolutional neural network for single-molecule nanopore sensing

TL;DR

This paper introduces QuipuNet, a CNN-based method that automates and improves the accuracy of analyzing nanopore sensing data for single-molecule detection, enabling faster and more reliable diagnostics.

Contribution

The paper presents a novel convolutional neural network that automates and enhances the analysis of nanopore signals, outperforming existing methods in accuracy and throughput.

Findings

Classifies translocation events with higher accuracy than previous methods.

Increases the number of analyzable events by a factor of five.

Demonstrates potential for rapid diagnostics using deep learning.

Abstract

Nanopore sensing is a versatile technique for the analysis of molecules on the single-molecule level. However, extracting information from data with established algorithms usually requires time-consuming checks by an experienced researcher due to inherent variability of solid-state nanopores. Here, we develop a convolutional neural network (CNN) for the fully automated extraction of information from the time-series signals obtained by nanopore sensors. In our demonstration, we use a previously published dataset on multiplexed single-molecule protein sensing. The neural network learns to classify translocation events with greater accuracy than previously possible, while also increasing the number of analysable events by a factor of five. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.