We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, 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 alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen 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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SquiggleNet：纳米孔信号的实时、直接分类

我们提出了 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 通过孔隙的速度更快，允许实时分类和读取弹出。使用 1 秒的测序数据，分类器的准确度明显高于碱基调用和序列比对。我们的方法也比基于对齐的方法更快，并且需要的内存少一个数量级。SquiggleNet 以超过 90% 的准确率将人类与细菌 DNA 区分开来，推广到人类呼吸道元基因组样本中看不见的细菌物种，并准确分类包含人类长散布重复元素的序列。