Fast-bonito: A faster deep learning based basecaller for nanopore sequencing

Fast-bonito: A faster deep learning based basecaller for nanopore sequencing

Blog Article

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 the gel bottle cashmere 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 here 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.