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Label Core for Understanding RNA Structure

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2019)

Abstract

The RNA structure, the main predictor of biological function, is the result of the folding process. While the nucleotides in the RNA sequence rapidly coupled forming weak bonds, the spatial arrangement is a slow process. Although many computational approaches have been proposed to study the folding process of RNA, most of them do not consider the hierarchical aspect existing among the bonds. In this work, we propose to collapse nucleotides and bonds underpinning the primary and secondary structure of RNA in a unique label core congruent with the spatial configuration. A label core is represented as a term of generalized context-free grammar properly defined to support RNA structural reduction and analysis.

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References

  1. Andersen, J.E., Huang, F.W., Penner, R., Reidys, C.: Topology of RNA-RNA interaction structures. J. Comput. Biol. 7(19), 928–943 (2012)

    Article  MathSciNet  Google Scholar 

  2. Andronescu, M., Bereg, V., Hoos, H.H., Condon, A.: RNA STRAND: the RNA secondary structure and statistical analysis database. BMC Bioinform. 9(1), 340 (2008)

    Article  Google Scholar 

  3. Giegerich, R., Steffen, P.: Implementing algebraic dynamic programming in the functional and the imperative programming paradigm. In: Boiten, E.A., Möller, B. (eds.) MPC 2002. LNCS, vol. 2386, pp. 1–20. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45442-X_1

    Chapter  Google Scholar 

  4. Grigoriev, A.: Analyzing genomes with cumulative skew diagrams. Nucl. Acids Res. 26(10), 2286–2290 (1998)

    Article  Google Scholar 

  5. Harrison, M.A.: Introduction to Formal Language Theory. Addison-Wesley Longman Publishing Co., Inc., Boston (1978)

    MATH  Google Scholar 

  6. Holbrook, S.R., Kim, S.H.: RNA crystallography. Biopolym.: Orig. Res. Biomol. 44(1), 3–21 (1997)

    Article  Google Scholar 

  7. Kasami, T., Seki, H., Fujii, M.: Generalized context-free grammars and multiple context-free grammars. Sys. Comput. Jpn. 20(7), 43–52 (1989)

    Article  MathSciNet  Google Scholar 

  8. Kjems, J., Egebjerg, J.: Modern methods for probing RNA structure. Curr. Opin. Biotechnol. 9(1), 59–65 (1998)

    Article  Google Scholar 

  9. Maestri, S., Merelli, E.: Process calculi may reveal the equivalence lying at the heart of RNA and proteins. Sci. Rep. 9(559), 1–9 (2019)

    Google Scholar 

  10. Quadrini, M., Tesei, L., Merelli, E.: An algebraic language for RNA pseudoknots comparison. BMC Bioinform. 20(4), 161 (2019)

    Article  Google Scholar 

  11. Quadrini, M., Merelli, E.: Loop-loop interaction metrics on RNA secondary structures with pseudoknots. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS, pp. 29–37 (2018)

    Google Scholar 

  12. Quadrini, M., Merelli, E., Piergallini, R.: Loop grammars to identify RNA structural patterns. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS, pp. 302–309. SciTePress (2019)

    Google Scholar 

  13. Reidys, C.: Combinatorial Computational Biology of RNA. Springer, New York (2011)

    Book  Google Scholar 

  14. Yousef, M., Khalifa, W., Acar, I.E., Allmer, J.: MicroRNA categorization using sequence motifs and k-mers. BMC Bioinform. 18(1), e170 (2017)

    Article  Google Scholar 

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Acknowledgements

We acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme (FP7) for Research of the European Commission, under the FET-Proactive grant agreement TOPDRIM (www.topdrim.eu), number FP7-ICT- 318121.

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Correspondence to Michela Quadrini .

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Appendix A

Appendix A

In this appendix we list the molecules of the two group that we have downloaded from the RNA STRAND database.

figure a

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Quadrini, M., Merelli, E., Piergallini, R. (2020). Label Core for Understanding RNA Structure. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-63061-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63060-7

  • Online ISBN: 978-3-030-63061-4

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