201. Fast-bonito: A faster deep learning based basecaller for nanopore sequencing
- Author
-
Zhimeng Xu, Yuting Mai, Denghui Liu, Wenjun He, Xinyuan Lin, Chi Xu, Lei Zhang, Xin Meng, Joseph Mafofo, Walid Abbas Zaher, Ashish Koshy, Yi Li, and Nan Qiao
- Subjects
Bonito ,Fast-Bonito ,Base calling ,Nanopore ,Ascend chip ,Deep neural network ,Science (General) ,Q1-390 - Abstract
Nanopore sequencing from Oxford Nanopore Technologies (ONT) is a promising third-generation sequencing (TGS) technology that generates relatively longer sequencing reads compared to the next-generation sequencing (NGS) technology. A basecaller is a piece of software that translates the original electrical current signals into nucleotide sequences. The accuracy of the basecaller is crucially important to downstream analysis. Bonito is a deep learning-based basecaller recently developed by ONT. Its neural network architecture is composed of a single convolutional layer followed by three stacked bidirectional gated recurrent unit (GRU) layers. Although Bonito has achieved state-of-the-art base calling accuracy, its speed is too slow to be used in production. We therefore developed Fast-Bonito, by using the neural architecture search (NAS) technique to search for a brand-new neural network backbone, and trained it from scratch using several advanced deep learning model training techniques. The new Fast-Bonito model balanced performance in terms of speed and accuracy. Fast-Bonito was 153.8% faster than the original Bonito on NVIDIA V100 GPU. When running on HUAWEI Ascend 910 NPU, Fast-Bonito was 565% faster than the original Bonito. The accuracy of Fast-Bonito was also slightly higher than that of Bonito. We have made Fast-Bonito open source, hoping it will boost the adoption of TGS in both academia and industry.
- Published
- 2021
- Full Text
- View/download PDF