Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model

• URL: http://arxiv.org/abs/2511.01277v1
• Date: Mon, 03 Nov 2025 06:51:53 GMT
• Title: Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model
• Authors: Annabelle Martin, Daphne Kontogiorgos-Heintz, Jeff Nivala,
• Abstract summary: We develop a lightweight one-dimensional convolutional neural network (1D CNN) to detect capture phases in down-sampled signal windows.<n>Our best model, CaptureNet-Deep, achieved an F1 score of 0.94 and precision of 93.39% on held-out test data.<n>These results show that efficient, real-time capture detection is possible using simple, interpretable architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nanopore protein sequencing produces long, noisy ionic current traces in which key molecular phases, such as protein capture and translocation, are embedded. Capture phases mark the successful entry of a protein into the pore and serve as both a checkpoint and a signal that a channel merits further analysis. However, manual identification of capture phases is time-intensive, often requiring several days for expert reviewers to annotate the data due to the need for domain-specific interpretation of complex signal patterns. To address this, a lightweight one-dimensional convolutional neural network (1D CNN) was developed and trained to detect capture phases in down-sampled signal windows. Evaluated against CNN-LSTM (Long Short-Term Memory) hybrids, histogram-based classifiers, and other CNN variants using run-level data splits, our best model, CaptureNet-Deep, achieved an F1 score of 0.94 and precision of 93.39% on held-out test data. The model supports low-latency inference and is integrated into a dashboard for Oxford Nanopore experiments, reducing the total analysis time from several days to under thirty minutes. These results show that efficient, real-time capture detection is possible using simple, interpretable architectures and suggest a broader role for lightweight ML models in sequencing workflows.

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