QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing.

Authors
Ermann, Niklas 
Keyser, Ulrich F 

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Article
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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 data set 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 analyzable events by a factor of 5. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.

Publication Date
2018-06-13
Online Publication Date
2018-06
Acceptance Date
2018-05-30
Keywords
q-bio.QM, q-bio.QM, physics.data-an, J.2; I.2.6; I.5.1
Journal Title
Nano Lett
Journal ISSN
1530-6984
1530-6992
Volume Title
18
Publisher
American Chemical Society (ACS)
Sponsorship
European Research Council (647144)