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Predicting RNA Secondary Structure Using In Vitro and In Vivo Data

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Part of the Methods in Molecular Biology book series (MIMB,volume 2404)

Abstract

The new flow of high-throughput RNA secondary structure data coming from different techniques allowed the further development of machine learning approaches. We developed CROSS and CROSSalive, two algorithms trained on experimental data able to predict the RNA secondary structure propensity both in vitro and in vivo. Since the in vivo folding of RNA molecules depends on multiple factors due to the cellular crowded environment, prediction is a complex problem that needs additional calculations for the interaction with proteins and other molecules. In the following chapter, we will describe the differences in predicting RNA secondary structure propensity using experimental data as input for an Artificial Neural Network (ANN) in vitro and in vivo.

Key words

  • Artificial neural networks
  • RNA secondary structure
  • Machine learning
  • SHAPE
  • RNA structure in vivo

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References

  1. Bellucci M, Agostini F, Masin M, Tartaglia GG (2011) Predicting protein associations with long noncoding RNAs. Nat Methods 8:444–445. https://doi.org/10.1038/nmeth.1611

    CAS  CrossRef  PubMed  Google Scholar 

  2. Zhang J, Ferré-D’Amaré AR (2014) Dramatic improvement of crystals of large RNAs by cation replacement and dehydration. Structure 22:1363–1371. https://doi.org/10.1016/j.str.2014.07.011

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  3. Kertesz M, Wan Y, Mazor E et al (2010) Genome-wide measurement of RNA secondary structure in yeast. Nature 467:103–107. https://doi.org/10.1038/nature09322

    CAS  CrossRef  PubMed  Google Scholar 

  4. Wilkinson KA, Merino EJ, Weeks KM (2006) Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution. Nat Protoc 1:1610–1616. https://doi.org/10.1038/nprot.2006.249

    CAS  CrossRef  PubMed  Google Scholar 

  5. Delli Ponti R, Marti S, Armaos A, Tartaglia GG (2017) A high-throughput approach to profile RNA structure. Nucleic Acids Res 45:e35. https://doi.org/10.1093/nar/gkw1094

    CAS  CrossRef  PubMed  Google Scholar 

  6. Zemora G, Waldsich C (2010) RNA folding in living cells. RNA Biol 7:634–641. https://doi.org/10.4161/rna.7.6.13554

    CrossRef  PubMed  PubMed Central  Google Scholar 

  7. Zhou H-X, Rivas G, Minton AP (2008) Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences. Annu Rev Biophys 37:375–397. https://doi.org/10.1146/annurev.biophys.37.032807.125817

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  8. Delli Ponti R, Armaos A, Vandelli A, Tartaglia GG (2020) CROSSalive: a web server for predicting the in vivo structure of RNA molecules. Bioinformatics 36:940–941. https://doi.org/10.1093/bioinformatics/btz666

    CAS  CrossRef  Google Scholar 

  9. Gruber AR, Lorenz R, Bernhart SH et al (2008) The Vienna RNA websuite. Nucleic Acids Res 36:W70–W74. https://doi.org/10.1093/nar/gkn188

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  10. Agostini F, Zanzoni A, Klus P et al (2013) catRAPID omics: a web server for large-scale prediction of protein-RNA interactions. Bioinformatics 29:2928–2930. https://doi.org/10.1093/bioinformatics/btt495

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  11. Wan Y, Qu K, Zhang QC et al (2014) Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505:706–709. https://doi.org/10.1038/nature12946

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  12. Watts JM, Dang KK, Gorelick RJ et al (2009) Architecture and secondary structure of an entire HIV-1 RNA genome. Nature 460:711–716. https://doi.org/10.1038/nature08237

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  13. Spitale RC, Flynn RA, Zhang QC et al (2015) Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519:486–490. https://doi.org/10.1038/nature14263

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  14. Zhang S-W, Wang Y, Zhang X-X, Wang J-Q (2019) Prediction of the RBP binding sites on lncRNAs using the high-order nucleotide encoding convolutional neural network. Anal Biochem 583:113364. https://doi.org/10.1016/j.ab.2019.113364

    CAS  CrossRef  PubMed  Google Scholar 

  15. Smola MJ, Christy TW, Inoue K et al (2016) SHAPE reveals transcript-wide interactions, complex structural domains, and protein interactions across the Xist lncRNA in living cells. Proc Natl Acad Sci U S A 113:10322–10327. https://doi.org/10.1073/pnas.1600008113

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  16. Brion P, Westhof E (1997) Hierarchy and dynamics of RNA folding. Annu Rev Biophys Biomol Struct 26:113–137. https://doi.org/10.1146/annurev.biophys.26.1.113

    CAS  CrossRef  PubMed  Google Scholar 

  17. Lowman HB, Draper DE (1986) On the recognition of helical RNA by cobra venom V1 nuclease. J Biol Chem 261:5396–5403

    CAS  CrossRef  Google Scholar 

  18. Liu J, Jia G (2014) Methylation modifications in eukaryotic messenger RNA. J Genet Genomics 41:21–33. https://doi.org/10.1016/j.jgg.2013.10.002

    CAS  CrossRef  PubMed  Google Scholar 

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Correspondence to Gian Gaetano Tartaglia .

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Delli Ponti, R., Tartaglia, G.G. (2022). Predicting RNA Secondary Structure Using In Vitro and In Vivo Data. In: Dassi, E. (eds) Post-Transcriptional Gene Regulation. Methods in Molecular Biology, vol 2404. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1851-6_2

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  • DOI: https://doi.org/10.1007/978-1-0716-1851-6_2

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1850-9

  • Online ISBN: 978-1-0716-1851-6

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