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

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.

SUBMITTER: Misiunas K

PROVIDER: S-EPMC6025884 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Misiunas Karolis K Ermann Niklas N Keyser Ulrich F UF

Nano letters 20180601 6

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 p ...[more]

PMID: 29845855

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