BaseFormer: Transformer based Base-Caller for Fast and Accurate Next Generation Sequencing

Shuwei Li; Zhiru Guo; Ao Shen; Zheqi Yu; Wei Mao; Shaobo Luo; Hao Yu

10.1109/EMBC48229.2022.9871730

2022

2375-7477

978-1-7281-2783-5

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

463-466

11-15 July 2022

Glasgow, Scotland, United Kingdom

Gene sequencing technology is a tool which greatly impacts modern biology and medicine. The next-generation sequencing (NGS) lies at the heart of gene sequencing for its massively increasing throughput, but it is difficult to analyze the large quantities of fluorescent images with high accuracy because the fluorescent signals are weak with varying noise signals, and current designs are limited on accuracy and speed. In this paper, we proposed a novel deep learning based gene sequencing pipeline with semi-automatic labelling method. The obtained results are promising, especially on the high-density data, as the BaseFormer surpasses the traditional methods in terms of cluster quality (Q30: 88 %), throughput (16.5% better), and with similar and low error rate (down to 0.137% on average, best at 0.068 % on high-density data).

gene sequencing deep learning transformer basecall

第一

IEEE

School of Microelectronics, Southern University of Science and Technology, Shenzhen, China

Shuwei Li,Zhiru Guo,Ao Shen,et al. BaseFormer: Transformer based Base-Caller for Fast and Accurate Next Generation Sequencing[C],2022:463-466.