Skip to main content

Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores

  • Protocol
  • First Online:
Computational Epigenomics and Epitranscriptomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2624))

Abstract

This chapter describes MasterOfPores v.2 (MoP2), an open-source suite of pipelines for processing and analyzing direct RNA Oxford Nanopore sequencing data. The MoP2 relies on the Nextflow DSL2 framework and Linux containers, thus enabling reproducible data analysis in transcriptomic and epitranscriptomic studies. We introduce the key concepts of MoP2 and provide a step-by-step fully reproducible and complete example of how to use the workflow for the analysis of S. cerevisiae total RNA samples sequenced using MinION flowcells. The workflow starts with the pre-processing of raw FAST5 files, which includes basecalling, read quality control, demultiplexing, filtering, mapping, estimation of per-gene/transcript abundances, and transcriptome assembly, with support of the GPU computing for the basecalling and read demultiplexing steps. The secondary analyses of the workflow focus on the estimation of RNA poly(A) tail lengths and the identification of RNA modifications. The MoP2 code is available at https://github.com/biocorecrg/MOP2 and is distributed under the MIT license.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brown CG, Clarke J (2016) Nanopore development at Oxford Nanopore. Nat Biotechnol 34(8):810–811. https://doi.org/10.1038/nbt.3622

    Article  CAS  PubMed  Google Scholar 

  2. Midha MK, Wu M, Chiu KP (2019) Long-read sequencing in deciphering human genetics to a greater depth. Hum Genet 138(11–12):1201–1215. https://doi.org/10.1007/s00439-019-02064-y

    Article  CAS  PubMed  Google Scholar 

  3. Mahmoud M, Gobet N, Cruz-Dávalos DI, Mounier N, Dessimoz C, Sedlazeck FJ (2019) Structural variant calling: the long and the short of it. Genome Biol 20(1):246. https://doi.org/10.1186/s13059-019-1828-7

    Article  PubMed  PubMed Central  Google Scholar 

  4. Liu C (2021) A long road/read to rapid high-resolution HLA typing: The nanopore perspective. Hum Immunol 82(7):488–495. https://doi.org/10.1016/j.humimm.2020.04.009

    Article  CAS  PubMed  Google Scholar 

  5. Krause M, Niazi AM, Labun K, Torres Cleuren YN, Müller FS, Valen E (2019) tailfindr: alignment-free poly(A) length measurement for oxford nanopore RNA and DNA sequencing. RNA 25(10):1229–1241. https://doi.org/10.1261/rna.071332.119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Workman RE, Tang AD, Tang PS et al (2019) Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat Methods 16(12):1297–1305. https://doi.org/10.1038/s41592-019-0617-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Niazi AM, Krause M, Valen E (2021) Transcript isoform-specific estimation of Poly(A) tail length by nanopore sequencing of native RNA. Methods Mol Biol 2284:543–567. https://doi.org/10.1007/978-1-0716-1307-8_30

    Article  CAS  PubMed  Google Scholar 

  8. Bolisetty MT, Rajadinakaran G, Graveley BR (2015) Determining exon connectivity in complex mRNAs by nanopore sequencing. Genome Biol 16:204. https://doi.org/10.1186/s13059-015-0777-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sessegolo C, Cruaud C, Da Silva C et al (2019) Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules. Sci Rep 9(1):14908. https://doi.org/10.1038/s41598-019-51470-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Byrne A, Beaudin AE, Olsen HE et al (2017) Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat Commun 8:16027. https://doi.org/10.1038/ncomms16027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Križanovic K, Echchiki A, Roux J, Šikic M (2018) Evaluation of tools for long read RNA-seq splice-aware alignment. Bioinformatics 34(5):748–754. https://doi.org/10.1093/bioinformatics/btx668

    Article  CAS  PubMed  Google Scholar 

  12. Carlsen AT, Zahid OK, Ruzicka JA, Taylor EW, Hall AR (2014) Selective detection and quantification of modified DNA with solid-state nanopores. Nano Lett 14(10):5488–5492. https://doi.org/10.1021/nl501340d

    Article  CAS  PubMed  Google Scholar 

  13. Furlan M, Delgado-Tejedor A, Mulroney L, Pelizzola M, Novoa EM, Leonardi T (2021) Computational methods for RNA modification detection from nanopore direct RNA sequencing data. RNA Bio 18:1–10. https://doi.org/10.1080/15476286.2021.1978215

    Article  CAS  Google Scholar 

  14. Leger A, Amaral PP, Pandolfini L et al (2021) RNA modifications detection by comparative Nanopore direct RNA sequencing. Nat Commun 12(1):7198. https://doi.org/10.1038/s41467-021-27393-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Simpson JT, Workman RE, Zuzarte PC, David M, Dursi LJ, Timp W (2017) Detecting DNA cytosine methylation using nanopore sequencing. Nat Methods 14(4):407–410. https://doi.org/10.1038/nmeth.4184

    Article  CAS  PubMed  Google Scholar 

  16. Garalde DR, Snell EA, Jachimowicz D et al (2018) Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods 15(3):201–206. https://doi.org/10.1038/nmeth.4577

    Article  CAS  PubMed  Google Scholar 

  17. Liu H, Begik O, Lucas MC et al (2019) Accurate detection of m6A RNA modifications in native RNA sequences. Nat Commun 10(1):4079. https://doi.org/10.1038/s41467-019-11713-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Parker MT, Knop K, Sherwood A et al (2020) Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m. Elife 9:e49658. https://doi.org/10.7554/eLife.49658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Haussmann IU, Bodi Z, Sanchez-Moran E et al (2016) m6A potentiates Sxl alternative pre-mRNA splicing for robust Drosophila sex determination. Nature 540(7632):301–304. https://doi.org/10.1038/nature20577

    Article  CAS  PubMed  Google Scholar 

  20. Madugalle SU, Meyer K, Wang DO, Bredy TW (2020) RNA N6-Methyladenosine and the regulation of RNA localization and function in the brain. Trends Neurosci 12;43(12):1011–1023. https://doi.org/10.1016/j.tins.2020.09.005

    Article  CAS  PubMed  Google Scholar 

  21. Yu J, Chen M, Huang H et al (2018) Dynamic m6A modification regulates local translation of mRNA in axons. Nucleic Acids Res 46(3):1412–1423. https://doi.org/10.1093/nar/gkx1182

    Article  CAS  PubMed  Google Scholar 

  22. Roundtree IA, Evans ME, Pan T, He C (2017) Dynamic RNA modifications in gene expression regulation. Cell 169(7):1187–1200. https://doi.org/10.1016/j.cell.2017.05.045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee Y, Choe J, Park OH, Kim YK (2020) Molecular mechanisms driving mRNA degradation by m6A modification. Trends Genet 36(3):177–188. https://doi.org/10.1016/j.tig.2019.12.007

    Article  CAS  PubMed  Google Scholar 

  24. Geula S, Moshitch-Moshkovitz S, Dominissini D et al (2015) Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347(6225):1002–1006. https://doi.org/10.1126/science.1261417

    Article  CAS  PubMed  Google Scholar 

  25. Lence T, Akhtar J, Bayer M et al (2016) m6A modulates neuronal functions and sex determination in Drosophila. Nature 540(7632):242–247. https://doi.org/10.1038/nature20568

    Article  CAS  PubMed  Google Scholar 

  26. Freund I, Eigenbrod T, Helm M, Dalpke AH (2019) RNA modifications modulate activation of innate toll-like receptors. Genes (Basel) 10(2). https://doi.org/10.3390/genes10020092

  27. Jonkhout N, Tran J, Smith MA, Schonrock N, Mattick JS, Novoa EM (2017) The RNA modification landscape in human disease. RNA 23(12):1754–1769. https://doi.org/10.1261/rna.063503.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Frye M, Harada BT, Behm M, He C (2018) RNA modifications modulate gene expression during development. Science 361(6409):1346–1349. https://doi.org/10.1126/science.aau1646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Barbieri I, Kouzarides T (2020) Role of RNA modifications in cancer. Nat Rev Cancer 20(6):303–322. https://doi.org/10.1038/s41568-020-0253-2

    Article  CAS  PubMed  Google Scholar 

  30. Yanas A, Liu KF (2019) RNA modifications and the link to human disease. Methods Enzymol 626:133–146. https://doi.org/10.1016/bs.mie.2019.08.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Courtney DG (2021) Post-transcriptional regulation of viral RNA through epitranscriptional modification. Cells 10(5). https://doi.org/10.3390/cells10051129

  32. Li N, Hui H, Bray B et al (2021) METTL3 regulates viral m6A RNA modification and host cell innate immune responses during SARS-CoV-2 infection. Cell Rep 35(6):109091. https://doi.org/10.1016/j.celrep.2021.109091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tsai K, Cullen BR (2020) Epigenetic and epitranscriptomic regulation of viral replication. Nat Rev Microbiol 18(10):559–570. https://doi.org/10.1038/s41579-020-0382-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Horova V, Landova B, Hodek J et al (2021) Localization of SARS-CoV-2 capping enzymes revealed by an antibody against the nsp10 Subunit. Viruses 13(8). https://doi.org/10.3390/v13081487

  35. Maldonado López A, Capell BC (2021) The METTL3-m6A Epitranscriptome: dynamic regulator of epithelial development, differentiation, and cancer. Genes (Basel) 12(7). https://doi.org/10.3390/genes12071019

  36. Zheng X, Wang J, Zhang X et al (2021) RNA m6A methylation regulates virus-host interaction and EBNA2 expression during Epstein-Barr virus infection. Immun Inflamm Dis 9(2):351–362. https://doi.org/10.1002/iid3.396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kennedy EM, Courtney DG, Tsai K, Cullen BR (2017) Viral epitranscriptomics. J Virol 91(9). https://doi.org/10.1128/JVI.02263-16

  38. Köster J, Rahmann S (2012) Snakemake–a scalable bioinformatics workflow engine. Bioinformatics 28(19):2520-2522. doi:https://doi.org/10.1093/bioinformatics/bts480

  39. Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C (2017) Nextflow enables reproducible computational workflows. Nat Biotechnol 35(4):316–319. https://doi.org/10.1038/nbt.3820

    Article  CAS  PubMed  Google Scholar 

  40. Crusoe MR, Abeln A, Alexandru I, Peter A, Community TC (2021) Methods included: standardizing computational reuse and portability with the common workflow language. arXiv 2105.07028 [cs.DC]; 2021

    Google Scholar 

  41. Jalili V, Afgan E, Gu Q et al (2020) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res 48(W1):W395–W402. https://doi.org/10.1093/nar/gkaa434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cozzuto L, Liu H, Pryszcz LP et al (2020) MasterOfPores: a workflow for the analysis of oxford nanopore direct RNA sequencing datasets. Front Genet 11:211. https://doi.org/10.3389/fgene.2020.00211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nextflow DSL2. https://www.nextflow.io/docs/latest/dsl2.html. Accessed 23 December 2021.

  44. Begik O, Lucas MC, Pryszcz LP et al (2021) Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing. Nat Biotechnol 39(10):1278–1291. https://doi.org/10.1038/s41587-021-00915-6

    Article  CAS  PubMed  Google Scholar 

  45. Smith MA, Ersavas T, Ferguson JM et al (2020) Molecular barcoding of native RNAs using nanopore sequencing and deep learning. Genome Res 30(9):1345–1353. https://doi.org/10.1101/gr.260836.120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cozzuto L, Di Tommaso P. BioNextflow – a library of Groovy classes for Nextflow. https://github.com/biocorecrg/BioNextflow/tree/0.7.3. Accessed 23 Dec 2021

  47. De Coster W, D’Hert S, Schultz DT, Cruts M, Van Broeckhoven C (2018) NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34(15):2666–2669. https://doi.org/10.1093/bioinformatics/bty149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Nanoq – ultra-fast quality control and summary reports for nanopore reads. https://github.com/esteinig/nanoq. Accessed 23 Dec 2021

  49. Lanfear R, Schalamun M, Kainer D, Wang W, Schwessinger B (2019) MinIONQC: fast and simple quality control for MinION sequencing data. Bioinformatics 35(3):523–525. https://doi.org/10.1093/bioinformatics/bty654

    Article  CAS  PubMed  Google Scholar 

  50. FastQC – a quality control tool for high throughput sequence data. Https://www.bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 23 Dec 2021

  51. Li H (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18):3094–3100. https://doi.org/10.1093/bioinformatics/bty191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. GraphMap2 – A highly sensitive and accurate mapper for long, error-prone reads. https://github.com/lbcb-sci/graphmap2. Accessed 23 Dec 2021

  53. Pryszcz L, Capella S. Bioinformatics binaries. https://github.com/lpryszcz/bin. Accessed 23 Dec 2021

  54. Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638

    Article  CAS  PubMed  Google Scholar 

  55. Gleeson J, Leger A, Prawer YDJ et al (2021) Accurate expression quantification from nanopore direct RNA sequencing with NanoCount. Nucleic Acids Res. https://doi.org/10.1093/nar/gkab1129

  56. bambu: reference-guided transcript discovery and quantification for long read RNA-Seq data. https://github.com/GoekeLab/bambu. https://doi.org/10.18129/B9.bioc.bambu. Accessed 23 Dec 2021

  57. Ewels P, Magnusson M, Lundin S, Käller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19):3047–3048. https://doi.org/10.1093/bioinformatics/btw354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Nanopolish – Software package for signal-level analysis of Oxford Nanopore sequencing data. https://github.com/jts/nanopolish. Accessed 23 Dec 2021

  59. Tombo – a suite of tools primarily for the identification of modified nucleotides from nanopore sequencing data. https://github.com/nanoporetech/tombo. Accessed 23 Dec 2021

  60. Delgdado-Tejedor A. NanoConsensus: consensus prediction of RNA modifications from direct RNA nanopore sequencing data. Zenodo. https://doi.org/10.5281/zenodo.5805806. Accessed 27 Dec 2021

Download references

Acknowledgments

This work was partly supported by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) (PID2021-128193NB-100 to EMN) and the European Research Council (ERC-StG-2021 No 101042103 to EMN). AD-T is supported by a Severo Ochoa FPI PhD fellowship from the MEIC (PRE2019-088498). We acknowledge the support of the MEIC to the EMBL partnership, Centro de Excelencia Severo Ochoa and CERCA Programme/Generalitat de Catalunya.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Eva Maria Novoa or Julia Ponomarenko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cozzuto, L., Delgado-Tejedor, A., Hermoso Pulido, T., Novoa, E.M., Ponomarenko, J. (2023). Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores. In: Oliveira, P.H. (eds) Computational Epigenomics and Epitranscriptomics. Methods in Molecular Biology, vol 2624. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2962-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2962-8_13

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2961-1

  • Online ISBN: 978-1-0716-2962-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics