DNA methylation is a common nucleotide modification, which is associated with various biological processes, such as gene expression and aging. Nanopore sequencing provides a direct detecting approach through searching specific current signal shifts. Recently, model-based approaches, especially those using deep learning models, have achieved significant performance improvements on nanopore methylation detection. In this work, we explore using the non-recurrent neural network structure of Bidirectional Encoder Representations from Transformers (BERT) for the task, which provides an alternative fast inference model to the state-of-the-art bi-directional Recurrent Neural Network (biRNN). In addition, we propose a refined BERT model with relative position representation and center hidden units concatenation, which takes account of the task-specific characters into modeling. We evaluate the proposed models on the R9 benchmark datasets of different motifs and methyltransferases. The experiment results show that the refined BERT model can achieve competitive or even better results than the state-of-the-art biRNN model, while the model inference speed is faster.

MOTIVATION: In recent years, nanopore sequencing technology has enabled inexpensive long-read sequencing, which promises reads longer than a few thousand bases. Such long-read sequences contribute to the precise detection of structural variations and accurate haplotype phasing. However, deciphering precise DNA sequences from noisy and complicated nanopore raw signals remains a crucial demand for downstream analyses based on higher-quality nanopore sequencing, although various basecallers have been introduced to date. RESULTS: To address this need, we developed a novel basecaller, Halcyon, that incorporates neural-network techniques frequently used in the field of machine translation. Our model employs monotonic-attention mechanisms to learn semantic correspondences between nucleotides and signal levels without any pre-segmentation against input signals. We evaluated performance with a human whole-genome sequencing dataset and demonstrated that Halcyon outperformed existing third-party basecallers and achieved competitive performance against the latest Oxford Nanopore Technologies' basecallers. AVAILABILITYAND IMPLEMENTATION: The source code (halcyon) can be found at https://github.com/relastle/halcyon.