Boza Vladimir, Peresini Peter, Brejova Brona, Vinar Tomas
IEEE/ACM Trans Comput Biol Bioinform. 2022 Nov-Dec;19(6):3416-3424. doi: 10.1109/TCBB.2021.3128366. Epub 2022 Dec 8.
In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.
在纳米孔测序中，当DNA分子通过测序孔时会测量电信号。将这些信号转化为DNA碱基（碱基识别）是一项极具挑战性的任务，其质量对测序准确性有很大影响。迄今为止，最成功的纳米孔碱基识别器使用卷积神经网络（CNN）来完成这项任务。CNN中的卷积层通常由具有固定窗口大小的滤波器组成，在分析具有均匀速度的信号时表现最佳。然而，纳米孔测序的速度在读取过程中和测序运行之间都有很大差异。在这里，我们提出了动态池化，这是一种新型神经网络组件，它通过自适应调整池化比例来解决这个问题。为了证明动态池化的有用性，我们开发了两个碱基识别器：苍鹭和鱼鹰。苍鹭的准确性超过了牛津纳米孔公司开发的实验性高精度碱基识别器博尼托。鱼鹰是一种快速碱基识别器，其准确性可与古比高精度模式相媲美，但不需要GPU加速，并且在普通桌面CPU上实现了近实时速度。可用性：https://github.com/fmfi-compbio/osprey，https://github.com/fmfi-compbio/heron。
