Transformer-based DNA methylation detection on ionic signals from Oxford Nanopore sequencing data

Transformer is an algorithm that adopts self-attention architecture in the neural networks and has been widely used in natural language processing. In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. We evaluated this idea using real data sets (Escherichia coli data and the human genome NA12878 sequenced by Simpson et al.) and demonstrated the ability of Transformers to detect methylation on ionic signal data.