m6ATM: a deep learning approach for unraveling transcriptome-wide m6A landscape in Nanopore sequencing data<br/>

Boyi Yu

[1P-191] m6ATM: a deep learning approach for unraveling transcriptome-wide m6A landscape in Nanopore sequencing data

^○Boyi Yu¹, Genta Nagae², Yutaka Midorikawa³, Kenji Tatsuno², Bhaskar Dasgupta¹, Satoshi Ota², Hiroyuki Aburatani², Hiroki Ueda¹ (1.Adv. Data Sci. Div., RCAST, Univ. of Tokyo, 2.Genome & Med. Div., RCAST, Univ. of Tokyo, 3.Dept. of Digestive Surgery, Nihon Univ. Sch. of Med.)

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The 46th Annual Meeting of the Molecular Biology Society of Japan

[1P-191] m6ATM: a deep learning approach for unraveling transcriptome-wide m6A landscape in Nanopore sequencing data

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