Halcyon: an accurate basecaller exploiting an encoder-decoder model with monotonic attention.
Bioinformatics
; 37(9): 1211-1217, 2021 06 09.
Article
en En
| MEDLINE
| ID: mdl-33165508
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.
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Bases de datos:
MEDLINE
Asunto principal:
Nanoporos
/
Secuenciación de Nanoporos
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article