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

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 the sequencing data of in vivo transcripts from Arabidopsis. DENA identifies 90% of miCLIP-detected m6A 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 m6A-deficient Arabidopsis, mtb and fip37-4, using Nanopore and evaluate their single-nucleotide m6A profiles using DENA.

Keywords

• DENA
• N 6 -Methyladenosine
• Nanopore direct RNA sequencing
• AtMTB
• AtFIP37
• Arabidopsis thaliana