DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for...

Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N⁶-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.