{"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/identifying-centromeric-satellites-with-dna","title":"Identifying centromeric satellites with dna-brnn","arxiv_id":"1901.07327","date":"2019-01-22","proceeding":null,"authors":["Heng Li"],"abstract":"Summary: Human alpha satellite and satellite 2/3 contribute to several\npercent of the human genome. However, identifying these sequences with\ntraditional algorithms is computationally intensive. Here we develop dna-brnn,\na recurrent neural network to learn the sequences of the two classes of\ncentromeric repeats. It achieves high similarity to RepeatMasker and is tens of\ntimes faster. Dna-brnn explores a novel application of deep learning and may\naccelerate the study of the evolution of the two repeat classes.\n  Availability and implementation: https://github.com/lh3/dna-nn\n  Contact: hli@jimmy.harvard.edu","url_abs":"http://arxiv.org/abs/1901.07327v1","url_pdf":"http://arxiv.org/pdf/1901.07327v1.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":"identifying-centromeric-satellites-with-dna","repo_url":"https://github.com/lh3/dna-nn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}