Abstract
RNA modifications regulate multiple aspects of cellular function including RNA splicing, translation, export, decay, stability, and phase separation. One of the comprehensive ways to detect such modifications is by the recent advancement of direct RNA sequencing from Oxford Nanopore Technologies (ONT). However, this method obtains a large amount of data with high complexity in the form of raw current signal that poses a new informatics challenge to accurately detect those modifications. Here, we provide nanoDoc2, a software to detect multiple types of RNA modification from nanopore direct RNA sequencing data. The nanoDoc2 includes a novel signal segmentation algorithm based on the trace value–a base probability feature that is added by the Guppy basecalling program from ONT during processing of the raw signal. The core of nanoDoc2 includes a machine learning algorithm in which a 6-mer segmented raw current signal is analyzed by deep one-class classification using a WaveNet-based neural network. As an output, an RNA modification is detected by a statistical score in each candidate position. Herein, we describe the detailed instructions on how to use nanoDoc2 for signal segmentation, train/test the neural network, and finally predict RNA modifications present in nanopore direct RNA sequencing data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Suzuki T (2021) The expanding world of tRNA modifications and their disease relevance. Nat Rev Mol Cell Biol 22:375–392. https://doi.org/10.1038/s41580-021-00342-0
Tang Y, Chen K, Song B et al (2021) M6A-atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome. Nucleic Acids Res 49:D134–D143. https://doi.org/10.1093/nar/gkaa692
Anreiter I, Mir Q, Simpson JT et al (2020) New twists in detecting mRNA modification dynamics. Trends Biotechnol 39(1):72–89
Jonkhout N, Tran J, Smith MA et al (2017) The RNA modification landscape in human disease. RNA 23:1754–1769. https://doi.org/10.1261/rna.063503.117
Kumar S, Mohapatra T (2021) Deciphering epitranscriptome: modification of mRNA bases provides a new perspective for post-transcriptional regulation of gene expression. Front Cell Dev Biol 9:1–22. https://doi.org/10.3389/fcell.2021.628415
Boccaletto P, Stefaniak F, Ray A et al (2022) MODOMICS: a database of RNA modification pathways. 2021 update. Nucleic Acids Res 50:D231–D235. https://doi.org/10.1093/nar/gkab1083
Dominissini D, Moshitch-Moshkovitz S, Schwartz S et al (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485:201–206. https://doi.org/10.1038/nature11112
Collin W, Limbach PA (2014) Mass spectrometry of modified RNAs: recent developments (Minireview). Analyst 15:34–48. https://doi.org/10.1039/c5an01797a.Mass
Sakurai M, Ueda H, Yano T et al (2014) A biochemical landscape of A-to-I RNA editing in the human brain transcriptome. Genome Res 24:522–534. https://doi.org/10.1101/gr.162537.113
Meyer KD, Saletore Y, Zumbo P et al (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149:1635–1646. https://doi.org/10.1016/j.cell.2012.05.003
Workman RE, Tang AD, Tang PS et al (2019) Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat Methods 16:1297–1305. https://doi.org/10.1038/s41592-019-0617-2
Loman NJ, Quick J, Simpson JT (2015) A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat Methods 12:733–735. https://doi.org/10.1038/nmeth.3444
Stoiber MH, Quick J, Egan R et al (2016) De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. bioRxiv 094672
Liu H, Begik O, Lucas MC et al (2019) Accurate detection of m6A RNA modifications in native RNA sequences. Nat Commun 10:4079. https://doi.org/10.1038/s41467-019-11713-9
Parker MT, Knop K, Sherwood AV et al (2020) Nanopore direct RNA sequencing maps the complexity of arabidopsis mRNA processing and m6A modification. elife 9:1–35. https://doi.org/10.7554/eLife.49658
Jenjaroenpun P, Wongsurawat T, Wadley TD et al (2021) Decoding the epitranscriptional landscape from native RNA sequences. Nucleic Acids Res 49:1–13. https://doi.org/10.1093/nar/gkaa620
Abebe JS, Price AM, Hayer KE et al (2022) DRUMMER-rapid detection of RNA modifications through comparative nanopore sequencing. Bioinformatics btac274
Maier KC, Gressel S, Cramer P, Schwalb B (2020) Native molecule sequencing by nano-ID reveals synthesis and stability of RNA isoforms. Genome Res 30:1332–1344. https://doi.org/10.1101/GR.257857.119
Leger A, Amaral PP, Pandolfini L et al (2021) RNA modifications detection by comparative Nanopore direct RNA sequencing. Nat Commun 12:1–17. https://doi.org/10.1038/s41467-021-27393-3
Pratanwanich PN, Yao F, Chen Y et al (2021) Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat Biotechnol 39:1394–1402. https://doi.org/10.1038/s41587-021-00949-w
Gao Y, Liu X, Wu B et al (2021) Quantitative profiling of N 6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing. Genome Biol 22:1–17. https://doi.org/10.1186/s13059-020-02241-7
Parker MT, Barton GJ, Simpson GG (2021) Yanocomp: robust prediction of m6A modifications in individual nanopore direct RNA reads. bioRxiv 06(15):448494
Hassan D, Acevedo D, Daulatabad SV et al (2022) Penguin: a tool for predicting pseudouridine sites in direct RNA nanopore sequencing data. Methods S1046-2023(22):00035–00034. https://doi.org/10.1016/j.ymeth.2022.02.005
Ueda H (2020) nanoDoc: RNA modification detection using Nanopore raw reads with deep one-class classification. bioRxiv 09(13):295089. https://doi.org/10.1101/2020.09.13.295089
Stephenson W, Razaghi R, Busan S et al (2022) Direct detection of RNA modifications and structure using single-molecule nanopore sequencing. Cell Genomics 2:100097. https://doi.org/10.1016/j.xgen.2022.100097
Kim D, Lee JY, Yang JS et al (2020) The architecture of SARS-CoV-2 transcriptome. Cell 181:914-921.e10. https://doi.org/10.1016/j.cell.2020.04.011
van den Burg GJJ, Williams CKI (2020) An evaluation of change point detection algorithms. arXiv 2003:06222
van den Oord A, Dieleman S, Zen H et al (2016) WaveNet: a generative model for raw audio. arXiv 1609:03499
Li H (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34:3094–3100. https://doi.org/10.1093/bioinformatics/bty191
Perera P, Patel VM (2019) Learning deep features for one-class classification. IEEE Trans Image Process 28:5450–5463. https://doi.org/10.1109/TIP.2019.2917862
Acknowledgments
We thank Dr. Hiroyuki Aburatani and Dr. Genta Nagae (the Research Center for Advanced Science and Technology, the University of Tokyo) and Dr. Tsutomu Suzuki and Mr. Ryo Noguchi (Department of Chemistry and Biotechnology, Graduate School of Engineering, the University of Tokyo) for productive discussions and helpful advice. This work was supported by Exploratory Research for Advanced Technology (ERATO; JPMJER2002) from the Japan Science and Technology Agency (JST).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Ueda, H., Dasgupta, B., Yu, By. (2023). RNA Modification Detection Using Nanopore Direct RNA Sequencing and nanoDoc2. In: Arakawa, K. (eds) Nanopore Sequencing. Methods in Molecular Biology, vol 2632. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2996-3_21
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2996-3_21
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2995-6
Online ISBN: 978-1-0716-2996-3
eBook Packages: Springer Protocols