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

Abstract 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, deci...

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Veröffentlicht in:Bioinformatics Jg. 37; H. 9; S. 1211 - 1217
Hauptverfasser: Konishi, Hiroki, Yamaguchi, Rui, Yamaguchi, Kiyoshi, Furukawa, Yoichi, Imoto, Seiya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 09.06.2021
Schlagworte:
DNA
High-Throughput Nucleotide Sequencing
Humans
Nanopore Sequencing
Nanopores
Original Papers
Sequence Analysis, DNA
Software
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Zusammenfassung:Abstract 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btaa953