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Through detecting the change of ion currency signals during a DNA\/RNA fragment\u2019s pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. Through utilizing deep neural networks, the-state-of-the art nanopore basecallers achieve basecalling accuracy in a range from 85% to 95%.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Result<\/jats:title>\n<jats:p>In this work, we proposed a novel basecalling approach from a perspective of instance segmentation. Different from previous approaches of doing typical sequence labeling, we formulated the basecalling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed basecaller URnano achieves competitive results on the in-species data, compared to the recently proposed CTC-featured basecallers.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-020-3459-0","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T01:03:42Z","timestamp":1587603822000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Nanopore basecalling from a perspective of instance segmentation"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5598-2521","authenticated-orcid":false,"given":"Yao-zhong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Arda","family":"Akdemir","sequence":"additional","affiliation":[]},{"given":"Georg","family":"Tremmel","sequence":"additional","affiliation":[]},{"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[]},{"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[]},{"given":"Tetsuo","family":"Shibuya","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Yamaguchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"issue":"6","key":"3459_CR1","first-page":"1256","volume":"19","author":"A Magi","year":"2017","unstructured":"Magi A, Semeraro R, Mingrino A, Giusti B, D\u2019aurizio R. Nanopore sequencing data analysis: state of the art, applications and challenges. Brief Bioinforma. 2017; 19(6):1256\u201372.","journal-title":"Brief Bioinforma"},{"issue":"1","key":"3459_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s13059-018-1462-9","volume":"19","author":"FJ Rang","year":"2018","unstructured":"Rang FJ, Kloosterman WP, de Ridder J. From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biol. 2018; 19(1):90.","journal-title":"Genome Biol"},{"issue":"1","key":"3459_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/bioinformatics\/btw569","volume":"33","author":"M David","year":"2016","unstructured":"David M, Dursi LJ, Yao D, Boutros PC, Simpson JT. Nanocall: an open source basecaller for oxford nanopore sequencing data. 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Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience. 2018; 7(5):037.","journal-title":"GigaScience"},{"key":"3459_CR7","doi-asserted-by":"publisher","unstructured":"Stoiber MH, Quick J, Egan R, Lee JE, Celniker SE, Neely R, Loman N, Pennacchio L, Brown JB. De novo identification of dna modifications enabled by genome-guided nanopore signal processing. BioRxiv. 2016:094672. https:\/\/doi.org\/10.1101\/094672.","DOI":"10.1101\/094672"},{"key":"3459_CR8","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention. Springer: 2015. p. 234\u201341. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3459_CR9","unstructured":"Kingma DP, Ba J. 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