{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mincall-minion-end2end-convolutional-deep","title":"MinCall - MinION end2end convolutional deep learning basecaller","arxiv_id":"1904.10337","date":"2019-04-22","proceeding":null,"authors":["Neven Miculinić","Marko Ratković","Mile Šikić"],"abstract":"The Oxford Nanopore Technologies's MinION is the first portable DNA\nsequencing device. It is capable of producing long reads, over 100 kBp were\nreported. However, it has significantly higher error rate than other methods.\nIn this study, we present MinCall, an end2end basecaller model for the MinION.\nThe model is based on deep learning and uses convolutional neural networks\n(CNN) in its implementation. For extra performance, it uses cutting edge deep\nlearning techniques and architectures, batch normalization and Connectionist\nTemporal Classification (CTC) loss. The best performing deep learning model\nachieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and\n1D reads.","url_abs":"http://arxiv.org/abs/1904.10337v1","url_pdf":"http://arxiv.org/pdf/1904.10337v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mincall-minion-end2end-convolutional-deep","repo_url":"https://github.com/nmiculinic/minion-basecaller","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.10337"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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