{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T09:32:29Z","timestamp":1781429549609,"version":"3.54.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["TRS T21-705\/20-N"],"award-info":[{"award-number":["TRS T21-705\/20-N"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-022-00387-x","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T12:31:45Z","timestamp":1671453105000},"page":"797-803","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":381,"title":["Symphonizing pileup and full-alignment for deep learning-based long-read variant calling"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6546-2324","authenticated-orcid":false,"given":"Zhenxian","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shumin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8560-3999","authenticated-orcid":false,"given":"Junhao","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy Wing-Sze","family":"Leung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tak-Wah","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9711-6533","authenticated-orcid":false,"given":"Ruibang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"387_CR1","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/nbt.4235","volume":"36","author":"R Poplin","year":"2018","unstructured":"Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983\u2013987 (2018).","journal-title":"Nat. Biotechnol."},{"key":"387_CR2","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1038\/s41467-019-09025-z","volume":"10","author":"R Luo","year":"2019","unstructured":"Luo, R., Sedlazeck, F. J., Lam, T.-W. & Schatz, M. C. A multi-task convolutional deep neural network for variant calling in single molecule sequencing. Nat. Commun. 10, 998 (2019).","journal-title":"Nat. Commun."},{"key":"387_CR3","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42256-020-0167-4","volume":"2","author":"R Luo","year":"2020","unstructured":"Luo, R. et al. Exploring the limit of using a deep neural network on pileup data for germline variant calling. Nat. Mach. Intell. 2, 220\u2013227 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"387_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-021-02472-2","volume":"22","author":"MU Ahsan","year":"2021","unstructured":"Ahsan, M. U., Liu, Q., Fang, L. & Wang, K. NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks. Genome Biol. 22, 261 (2021).","journal-title":"Genome Biol."},{"key":"387_CR5","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1038\/s41592-021-01299-w","volume":"18","author":"K Shafin","year":"2021","unstructured":"Shafin, K. et al. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nat. Methods 18, 1322\u20131332 (2021).","journal-title":"Nat. Methods"},{"key":"387_CR6","unstructured":"Medaka, https:\/\/github.com\/nanoporetech\/medaka (2018)."},{"key":"387_CR7","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1089\/cmb.2014.0157","volume":"22","author":"M Patterson","year":"2015","unstructured":"Patterson, M. et al. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J. Comput. Biol. 22, 498\u2013509 (2015).","journal-title":"J. Comput. Biol."},{"key":"387_CR8","doi-asserted-by":"publisher","first-page":"4660","DOI":"10.1038\/s41467-019-12493-y","volume":"10","author":"P Edge","year":"2019","unstructured":"Edge, P. & Bansal, V. Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing. Nat. Commun. 10, 4660 (2019).","journal-title":"Nat. Commun."},{"key":"387_CR9","doi-asserted-by":"publisher","first-page":"100129","DOI":"10.1016\/j.xgen.2022.100129","volume":"2","author":"ND Olson","year":"2022","unstructured":"Olson, N. D. et al. PrecisionFDA Truth Challenge V2: calling variants from short and long reads in difficult-to-map regions. Cell Genomics 2, 100129 (2022).","journal-title":"Cell Genomics"},{"key":"387_CR10","doi-asserted-by":"publisher","first-page":"100128","DOI":"10.1016\/j.xgen.2022.100128","volume":"2","author":"J Wagner","year":"2022","unstructured":"Wagner, J. et al. Benchmarking challenging small variants with linked and long reads. Cell Genomics 2, 100128 (2022).","journal-title":"Cell Genomics"},{"key":"387_CR11","unstructured":"Nanopore EPI2ME Labs, https:\/\/labs.epi2me.io\/gm24385_2021.05\/ (2021)."},{"key":"387_CR12","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1038\/s41587-020-0503-6","volume":"38","author":"K Shafin","year":"2020","unstructured":"Shafin, K. et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nat. Biotechnol. 38, 1044\u20131053 (2020).","journal-title":"Nat. Biotechnol."},{"key":"387_CR13","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41587-019-0054-x","volume":"37","author":"P Krusche","year":"2019","unstructured":"Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37, 555\u2013560 (2019).","journal-title":"Nat. Biotechnol."},{"key":"387_CR14","unstructured":"Medaka v1.5.0, https:\/\/github.com\/nanoporetech\/medaka\/releases\/tag\/v1.5.0 (2021)."},{"key":"387_CR15","unstructured":"PEPPER r0.7, https:\/\/github.com\/kishwarshafin\/pepper\/releases\/tag\/r0.7 (2021)."},{"key":"387_CR16","doi-asserted-by":"publisher","unstructured":"Zheng, Z. et al. Symphonizing pileup and full-alignment for deep learning-based long-read variant calling. Zenodo https:\/\/doi.org\/10.5281\/zenodo.6637001 (2022).","DOI":"10.5281\/zenodo.6637001"},{"key":"387_CR17","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904\u20131916 (2015).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"387_CR18","unstructured":"Rerio, https:\/\/github.com\/nanoporetech\/rerio (2021)."},{"key":"387_CR19","unstructured":"Liu, L. et al. On the variance of the adaptive learning rate and beyond. Preprint at https:\/\/arxiv.org\/abs\/1908.03265 (2019)."},{"key":"387_CR20","unstructured":"Zhang, M. R., Lucas, J., Hinton, G. & Ba, J. Lookahead optimizer: k steps forward, 1 step back. Preprint at https:\/\/arxiv.org\/abs\/1907.08610 (2019)."}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00387-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00387-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00387-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T12:38:32Z","timestamp":1671453512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00387-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":20,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["387"],"URL":"https:\/\/doi.org\/10.1038\/s43588-022-00387-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1881940\/v1","asserted-by":"object"},{"id-type":"doi","id":"10.1101\/2021.12.29.474431","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]},"assertion":[{"value":"21 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"R.L. receives research funding from ONT. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}