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A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces Cerevisae

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Research in Computational Molecular Biology (RECOMB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10812))

  • 2017 Accesses

Abstract

The growing number of RNA-mediated regulation mechanisms identified in the last decades suggests a widespread impact of RNA-RNA interactions. The efficiency of the regulation relies on highly specific and coordinated interactions, while simultaneously repressing the formation of opportunistic complexes. However, the analysis of RNA interactomes is highly challenging due to the large number of potential partners, discrepancy of the size of RNA families, and the inherent noise in interaction predictions.

We designed a recursive 2-step cross-validation pipeline to capture the specificity of ncRNA-mRNA interactomes. Our method has been designed to detect significant loss or gain of specificity between ncRNA-mRNA interaction profiles. Applied to snoRNA-mRNA in Saccharomyces Cerevisae, our results suggest the existence of a repression of ncRNA affinities with mRNAs, and thus the existence of an evolutionary pressure inhibiting such interactions.

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References

  1. He, L., Hannon, G.J.: MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet. 5(7), 522–531 (2004). https://doi.org/10.1038/nrg1379

    Article  Google Scholar 

  2. Altuvia, S., Zhang, A., Argaman, L., Tiwari, A., Storz, G.: The Escherichia coli OxyS regulatory RNA represses fhlA translation by blocking ribosome binding. EMBO J. 17(20), 6069–6075 (1998). https://doi.org/10.1093/emboj/17.20.6069

    Article  Google Scholar 

  3. Scott, M.S., Ono, M.: From snoRNA to miRNA: dual function regulatory non-coding RNAs. Biochimie 93(11), 1987–1992 (2011). https://doi.org/10.1016/j.biochi.2011.05.026

    Article  Google Scholar 

  4. Sharma, E., Sterne-Weiler, T., O’Hanlon, D., Blencowe, B.J.: Global mapping of human RNA-RNA interactions. Mol. Cell 62(4), 618–626 (2016). https://doi.org/10.1016/j.molcel.2016.04.030

    Article  Google Scholar 

  5. Nguyen, T.C., Cao, X., Yu, P., Xiao, S., Lu, J., Biase, F.H., Sridhar, B., Huang, N., Zhang, K., Zhong, S.: Mapping RNA-RNA interactome and RNA structure in vivo by MARIO. Nat. Commun. 7, 12023 (2016). https://doi.org/10.1038/ncomms12023

    Article  Google Scholar 

  6. Panni, S., Prakash, A., Bateman, A., Orchard, S.: The yeast noncoding RNA interaction network. RNA 23(10), 1479–1492 (2017). https://doi.org/10.1261/rna.060996.117

    Article  Google Scholar 

  7. Aw, J.G.A., Shen, Y., Wilm, A., Sun, M., Lim, X.N., Boon, K.L., Tapsin, S., Chan, Y.S., Tan, C.P., Sim, A.Y.L., Zhang, T., Susanto, T.T., Fu, Z., Nagarajan, N., Wan, Y.: In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell 62(4), 603–617 (2016). https://doi.org/10.1016/j.molcel.2016.04.028

    Article  Google Scholar 

  8. Mattick, J.S.: RNA regulation: a new genetics? Nat. Rev. Genet. 5(4), 316–323 (2004). https://doi.org/10.1038/nrg1321

    Article  Google Scholar 

  9. Weill, N., Lisi, V., Scott, N., Dallaire, P., Pelloux, J., Major, F.: MiRBooking simulates the stoichiometric mode of action of microRNAs. Nucleic Acids Res. 43(14), 6730–6738 (2015). https://doi.org/10.1093/nar/gkv619

    Article  Google Scholar 

  10. Umu, S.U., Poole, A.M., Dobson, R.C., Gardner, P.P.: Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea. Elife 5 (2016). https://doi.org/10.7554/eLife.13479

  11. Waters, L.S., Storz, G.: Regulatory RNAs in bacteria. Cell 136(4), 615–628 (2009). https://doi.org/10.1016/j.cell.2009.01.043. http://www.sciencedirect.com/science/article/pii/S0092867409001251

    Article  Google Scholar 

  12. Storz, G., Vogel, J., Wassarman, K.M.: Regulation by small RNAs in bacteria: expanding frontiers. Mol. Cell 43(6), 880–891 (2011). https://doi.org/10.1016/j.molcel.2011.08.022. http://www.sciencedirect.com/science/article/pii/S1097276511006435

    Article  Google Scholar 

  13. Sherman, D., Durrens, P., Beyne, E., Nikolski, M., Souciet, J.L.: Génolevures: comparative genomics and molecular evolution of hemiascomycetous yeasts. Nucleic Acids Res. 32(Database Issue), D315–D318 (2004). https://doi.org/10.1093/nar/gkh091. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC308825/

    Article  Google Scholar 

  14. Mückstein, U., Tafer, H., Hackermüller, J., Bernhart, S.H., Stadler, P.F., Hofacker, I.L.: Thermodynamics of RNA-RNA binding. Bioinformatics 22(10), 1177–1182 (2006)

    Article  Google Scholar 

  15. Wright, P.R., Georg, J., Mann, M., Sorescu, D.A., Richter, A.S., Lott, S., Kleinkauf, R., Hess, W.R., Backofen, R.: CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains. NAR 42(Web Server Issue), W119–W123 (2014). https://doi.org/10.1093/nar/gku359. PRW, JG and MM contributed equally to this

    Article  Google Scholar 

  16. Thuriaux, P., Martin, C., Blondel, L., Visset, D.: Les organismes modèles: la levure. Belin, Paris (2004)

    Google Scholar 

  17. Busch, A., Richter, A.S., Backofen, R.: IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions. Bioinformatics 24(24), 2849–2856 (2008). https://doi.org/10.1093/bioinformatics/btn544

    Article  Google Scholar 

  18. Tafer, H., Kehr, S., Hertel, J., Hofacker, I.L., Stadler, P.F.: RNAsnoop: efficient target prediction for H/ACA snoRNAs. Bioinformatics 26(5), 610–616 (2010). https://doi.org/10.1093/bioinformatics/btp680

    Article  Google Scholar 

  19. Lai, D., Meyer, I.M.: A comprehensive comparison of general RNA-RNA interaction prediction methods. Nucleic Acids Res. 44(7), e61 (2016)

    Article  Google Scholar 

  20. Umu, S.U., Gardner, P.P.: A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life. Bioinformatics 33(7), 988–996 (2017)

    Google Scholar 

  21. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7

    Book  MATH  Google Scholar 

  22. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, McGill University, Montréal, Canada

    Antoine Soulé & Jérôme Waldispühl

  2. LIX - UMR 7161, École Polytechnique, Palaiseau, France

    Antoine Soulé & Jean-Marc Steyaert

Authors
  1. Antoine Soulé
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  2. Jean-Marc Steyaert
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  3. Jérôme Waldispühl
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Corresponding author

Correspondence to Jérôme Waldispühl .

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Editors and Affiliations

  1. Computer Science Department, Princeton University, Princeton, New Jersey, USA

    Benjamin J. Raphael

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Soulé, A., Steyaert, JM., Waldispühl, J. (2018). A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces Cerevisae. In: Raphael, B. (eds) Research in Computational Molecular Biology. RECOMB 2018. Lecture Notes in Computer Science(), vol 10812. Springer, Cham. https://doi.org/10.1007/978-3-319-89929-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-89929-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89928-2

  • Online ISBN: 978-3-319-89929-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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