Oxford Nanopore sequencers provide results in real time as DNA passes through a nanopore and can eject a molecule after it has been partly sequenced. However, the computational challenge of deciding whether to keep or reject a molecule in real time has limited the application of this capability. We present SquiggleNet, the first deep learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than the DNA passes through the pore, allowing real-time classification and read ejection. When given the amount of sequencing data generated in one second, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than approaches based on alignment. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen species, identified bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

中文翻译：

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使用 SquiggleNet 对纳米孔信号进行实时、直接分类

Oxford Nanopore 测序仪可在 DNA 通过纳米孔时实时提供结果，并在分子被部分测序后弹出分子。然而，决定是否实时保留或拒绝分子的计算挑战限制了这种能力的应用。我们展示了 SquiggleNet，这是第一个可以直接从电信号中对纳米孔读数进行分类的深度学习模型。SquiggleNet 的运行速度比 DNA 通过孔的速度更快，从而允许实时分类和读取弹出。当给定在一秒内生成的测序数据量时，分类器实现的准确度明显高于碱基识别和序列比对。我们的方法也更快，并且比基于对齐的方法需要的内存少一个数量级。