{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T23:32:56Z","timestamp":1774999976760,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u2019 basecallers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>The source code (halcyon) can be found at https:\/\/github.com\/relastle\/halcyon.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa953","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T20:13:27Z","timestamp":1604088807000},"page":"1211-1217","source":"Crossref","is-referenced-by-count":27,"title":["Halcyon: an accurate basecaller exploiting an encoder\u2013decoder model with monotonic attention"],"prefix":"10.1093","volume":"37","author":[{"given":"Hiroki","family":"Konishi","sequence":"first","affiliation":[{"name":"Health Intelligence Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Human Genome Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiyoshi","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Advanced Clinical Research Center, Institute of Medical Science, The University of Tokyo , Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoichi","family":"Furukawa","sequence":"additional","affiliation":[{"name":"Advanced Clinical Research Center, Institute of Medical Science, The University of Tokyo , Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2989-308X","authenticated-orcid":false,"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[{"name":"Health Intelligence Center"},{"name":"Human Genome Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"2023051706070003900_btaa953-B1","author":"Abadi","year":"2016"},{"key":"2023051706070003900_btaa953-B2","author":"Bahdanau","year":"2015"},{"key":"2023051706070003900_btaa953-B3","first-page":"1171","author":"Bengio","year":"2015"},{"key":"2023051706070003900_btaa953-B4","doi-asserted-by":"crossref","first-page":"e0178751","DOI":"10.1371\/journal.pone.0178751","article-title":"DeepNano: deep recurrent neural networks for base calling in MinION Nanopore reads","volume":"12","author":"Bo\u017ea","year":"2017","journal-title":"PLoS One"},{"key":"2023051706070003900_btaa953-B5","author":"Chen","year":"2018"},{"key":"2023051706070003900_btaa953-B6","first-page":"4774","author":"Chiu","year":"2018"},{"key":"2023051706070003900_btaa953-B7","first-page":"577","author":"Chorowski","year":"2015"},{"key":"2023051706070003900_btaa953-B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-01343-4","article-title":"Mapping and phasing of structural variation in patient genomes using nanopore sequencing","volume":"8","author":"Cretu Stancu","year":"2017","journal-title":"Nat. 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