DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA
Hang Qin, Liang Ou, Jian Gao, Longxian Chen, Jiawei Wang, Pei Hao, Xuan Li
Abstract
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 .