DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA

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 t...

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Autores principales: Qin, Hang, Ou, Liang, Gao, Jian, Chen, Longxian, Wang, Jia-Wei, Hao, Pei, Li, Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762864/
https://www.ncbi.nlm.nih.gov/pubmed/35039061
http://dx.doi.org/10.1186/s13059-021-02598-3
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author Qin, Hang
Ou, Liang
Gao, Jian
Chen, Longxian
Wang, Jia-Wei
Hao, Pei
Li, Xuan
author_facet Qin, Hang
Ou, Liang
Gao, Jian
Chen, Longxian
Wang, Jia-Wei
Hao, Pei
Li, Xuan
author_sort Qin, Hang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02598-3.
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spelling pubmed-87628642022-01-18 DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA Qin, Hang Ou, Liang Gao, Jian Chen, Longxian Wang, Jia-Wei Hao, Pei Li, Xuan Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02598-3. BioMed Central 2022-01-17 /pmc/articles/PMC8762864/ /pubmed/35039061 http://dx.doi.org/10.1186/s13059-021-02598-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Qin, Hang
Ou, Liang
Gao, Jian
Chen, Longxian
Wang, Jia-Wei
Hao, Pei
Li, Xuan
DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title_full DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title_fullStr DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title_full_unstemmed DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title_short DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA
title_sort dena: training an authentic neural network model using nanopore sequencing data of arabidopsis transcripts for detection and quantification of n(6)-methyladenosine on rna
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762864/
https://www.ncbi.nlm.nih.gov/pubmed/35039061
http://dx.doi.org/10.1186/s13059-021-02598-3
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