Halcyon: an accurate basecaller exploiting an encoder-decoder model with monotonic attention

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.

Automated summary

Recent advancements in nanopore sequencing technology have enabled cost-effective long-read sequencing, making precise DNA sequence analysis increasingly important. Researchers have developed a new basecaller, Halcyon, incorporating neural network techniques and monotonic-attention mechanisms to learn semantic correspondences between nucleotides and signal levels. Evaluation with human whole-genome sequencing data showed that Halcyon outperformed existing basecallers and achieved competitive performance against the latest basecallers from Oxford Nanopore Technologies.