DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA

作者：Hang Qin, Liang Ou, Jian Gao, Longxian Chen, Jiawei Wang, Pei Hao, Xuan Li · 发表于：Genome biology · 年份：2022 · DOI：10.1186/s13059-021-02598-3 · 被引用次数：83 · 研究领域：RNA modifications and cancer、Cancer-related molecular mechanisms research、RNA Research and Splicing

Abstract Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N 6 -methyladenosine (m 6 A) are likely distorted due to superimposed signals from saturated m 6 A residues. Here, we develop a neural network, DENA , for m 6 A quantification using the sequencing data of in vivo transcripts from Arabidopsis. DENA identifies 90% of miCLIP-detected m 6 A sites in Arabidopsis and obtains modification rates in human consistent to those found by SCARLET , demonstrating its robustness across species. We sequence the transcriptome of two additional m 6 A-deficient Arabidopsis, mtb and fip37-4 , using Nanopore and evaluate their single-nucleotide m 6 A profiles using DENA .