Fast-Bonito: A Faster Basecaller for Nanopore Sequencing
Xu, Z.; Mai, Y.; Liu, D.; He, W.; Lin, X.; Xu, C.; Zhang, L.; Meng, X.; Mafofo, J.; Zaher, W. A.; Li, Y.; Qiao, N.
Show abstract
Oxford Nanopore Technologies (ONT) is a promising sequencing technology that could generate relatively longer sequencing reads compared to the next generation sequencing (NGS) technology. The base calling process is very important for TGS. It translates the original electrical signals from the sequencer to the nucleotide sequence. By doing that, the base calling could significantly influence the accuracy of downstream analysis. Bonito is a recently developed basecaller based on deep neuron network, the neuron network architecture of which is composed of a single convolutional layer followed by three stacked bidirectional GRU layers. Although Bonito achieved the state-of-the-art accuracy, its speed is so slow that it is not likely to be used in production. We therefore implement Fast-Bonito, which introduces systematic optimization to speed up Bonito. Fast-Bonito archives 53.8% faster than the original version on NVIDIA V100 and could be further speed up by HUAWEI Ascend 910 NPU, achieving 565% faster than the original version. The accuracy of Fast-Bonito is also slightly higher than the original Bonito.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Clair: Exploring the limit of using a deep neural network on pileup data for germline variant calling 93%
- Improving protein function prediction with synthetic feature samples created by generative adversarial networks 92%
- Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks 92%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.