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

Li, X.; Qin, H.; Ou, L.; Gao, J.; Chen, L.; Hao, P.; Wang, J.

doi:10.1101/2021.12.29.474495

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bioRxiv · 10.1101/2021.12.29.474495

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

Li, X.·Qin, H.·Ou, L.·Gao, J.·Chen, L.·Hao, P.·Wang, J.

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Abstract

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.

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Li, X., Qin, H., Ou, L., Gao, J., Chen, L., Hao, P., Wang, J.. 2021-12-30. DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA. https://doi.org/10.1101/2021.12.29.474495

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