An attention-based neural network basecaller for Oxford Nanopore sequencing data

摘要

Highly portable Oxford Nanopore sequencer producing long reads in real time at low cost has made many breakthroughts in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. Here we develops SACall, an end-to-end basecaller based on convolution layers, transformer self-attention layers and CTC decoder. From the perspective of read accuracy, SACall yields better performance in the benchmark than ONT official basecaller Guppy and Albacore. SACall is an open-source, freely available basecaller, which gives a chance for researchers to train new basecalling models on specific data and basecall Nanopore reads.