DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N 6-methyladenosine on RNA

DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N 6-methyladenosine on RNA

Abstract Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N 6-methyladenosine (m6A) are likely distorted due to superimposed signals from saturated m6A residues. Here, we develop a neural network, DENA, for m6A quantification using...

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Bibliographic Details
Main Authors: Hang Qin, Liang Ou, Jian Gao, Longxian Chen, Jia-Wei Wang, Pei Hao, Xuan Li
Format: Article
Language:English
Published: BMC 2022-01-01
Series:Genome Biology
Subjects:
DENA
N 6 -Methyladenosine
Nanopore direct RNA sequencing
AtMTB
AtFIP37
Arabidopsis thaliana
Online Access:https://doi.org/10.1186/s13059-021-02598-3

Internet

https://doi.org/10.1186/s13059-021-02598-3

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