用轻量深度学习模型自动识别纳米孔蛋白质测序中的捕获阶段。
Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model
- 采用一维卷积网络，通过下采样信号窗口检测捕获阶段。
- 在测试集上达到0.94的F1分数和93.39%精确率。
- 模型低延迟可部署，分析时间从数天缩短至半小时内。
纳米孔蛋白质测序产生长而嘈杂的离子电流信号，其中嵌入了蛋白捕获、穿膜等关键分子阶段。捕获阶段标志蛋白成功进入孔道，既是质量检查点，也是值得进一步分析的信号。但人工识别耗时极长，专家需数日才能完成标注，因需对复杂信号模式进行领域特异性解读。为此，本文开发并训练了一种轻量级一维卷积神经网络（1D CNN），用于在下采样信号窗口中检测捕获阶段。在运行级别数据划分下，与CNN-LSTM混合模型、基于直方图的分类器及其他CNN变体对比，最优模型CaptureNet-Deep在独立测试集上取得0.94的F1分数和93.39%的精确率。模型支持低延迟推理，并已集成至牛津纳米孔实验仪表板，将总分析时间从数天压缩至不足三十分钟。结果表明，利用简单且可解释的架构即可实现高效实时捕获检测，暗示轻量级机器学习模型在测序流程中具有更广泛的应用潜力。
原文摘要 · Abstract (English)
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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