QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing
Open accessAbstract
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.
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Through combination of nanopore technology with deep learning methods, real-time nanopore data analysis is attainable.
“Compared with fluorescence, sequencing, or gel electrophoresis-based strategies, single-molecule nanopore measurements require less material and enable faster data reading; through a combination of this technology with deep learning methods,131 real-time nanopore data analysis is attainable.”
From Emerging Approaches to DNA Data Storage: Challenges and Prospects (2022) · cites this paper as [131]
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- Authors
- Misiunas, K., Ermann, N., & Keyser, U. F.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Misiunas, Karolis
- Keyser, Ulrich F.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-05-30
- Volume
- 18
- Issue
- 6
- Pages
- 4040-4045
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- PMC
- PMC6025884
- OpenAlex
- W2794569677
- Cited by
- 82
- References
- 28
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 8 · 2023: 7 · 2022: 11 · 2021: 13 · 2020: 12 · 2019: 9 · 2018: 2
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nano Letters (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Apollo (cc-by)
- Funders
- H2020 European Research Council (647144)
- Engineering and Physical Sciences Research Council (NA)
- Cambridge Trust, University of Cambridge (NA)
- Trinity Hall, University of Cambridge (NA)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Nanopore and Nanochannel Transport Studies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Molecular Junctions and Nanostructures
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Force Microscopy Techniques and Applications
- Keywords
- nanopore sensing, single-molecule detection, convolutional neural networks, time-series signal analysis, solid-state nanopores, automated data analysis, rapid diagnostics
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