Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data
DNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing data
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-10168-2 |
| _version_ | 1818977936504520704 |
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| author | Qian Liu Li Fang Guoliang Yu Depeng Wang Chuan-Le Xiao Kai Wang |
| author_facet | Qian Liu Li Fang Guoliang Yu Depeng Wang Chuan-Le Xiao Kai Wang |
| author_sort | Qian Liu |
| collection | DOAJ |
| description | DNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing data |
| first_indexed | 2024-12-20T16:35:40Z |
| format | Article |
| id | doaj.art-2a64c8015b9e434c94ff1733e2720d34 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-20T16:35:40Z |
| publishDate | 2019-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-2a64c8015b9e434c94ff1733e2720d342022-12-21T19:33:10ZengNature PortfolioNature Communications2041-17232019-06-0110111110.1038/s41467-019-10168-2Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing dataQian Liu0Li Fang1Guoliang Yu2Depeng Wang3Chuan-Le Xiao4Kai Wang5Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of PhiladelphiaRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of PhiladelphiaState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen UniversityGrandomics BiosciencesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen UniversityRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of PhiladelphiaDNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing datahttps://doi.org/10.1038/s41467-019-10168-2 |
| spellingShingle | Qian Liu Li Fang Guoliang Yu Depeng Wang Chuan-Le Xiao Kai Wang Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data Nature Communications |
| title | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data |
| title_full | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data |
| title_fullStr | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data |
| title_full_unstemmed | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data |
| title_short | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data |
| title_sort | detection of dna base modifications by deep recurrent neural network on oxford nanopore sequencing data |
| url | https://doi.org/10.1038/s41467-019-10168-2 |
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