In-silico Analysis of LncRNA-mRNA Target Prediction

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Long noncoding RNAs (lncRNAs) constitutes a class of noncoding RNAs which are versatile molecules and perform various regulatory functions. Hence, identifying its target mRNAs is an important step in predicting the functions of these molecules. Current lncRNA target prediction tools are not efficient enough to identify lncRNA-mRNA interactions accurately. The reliability of these methods is an issue, as interaction site detections are inaccurate quite often. In this paper our aim is to predict the lncRNA-mRNA interactions efficiently, incorporating the sequence, structure, and energy-based features of the lncRNAs and mRNAs. A brief study on the existing tools for RNA-RNA interaction helped us to understand the different binding sites, and after compiling the tools, we have modified the algorithms to detect the accessible sites and their energies for each interacting RNA sequence. Further RNAstructure tool is used to get the hybrid interaction structure for the accessible lncRNA and mRNA sites. It is found that our target prediction tool gives a better accuracy over the existing tools, after encompassing the sequence, structure, and energy features.

Keywords

Long non-coding RNA Accessible sites RNA structure Target prediction Machine learning 

Notes

Acknowledgements

We would like to thank Dr. Zhumur Ghosh (Assistant Professor, Bose Institute) and Sibun Parida (Research Associate, Bioinformatics Center) for their valuable support.

References

  1. 1.
  2. 2.
    Lee, J.T., Bartolomei, M.S.: c, imprinting, and long noncoding RNAs in health and disease. Cell 152(6), 1308–1323 (2013)CrossRefGoogle Scholar
  3. 3.
    Rinn, J.L., Chang, H.Y.: Genome regulation by long noncoding RNAs. Annual Rev. Biochem. 81, 145–166 (2012)CrossRefGoogle Scholar
  4. 4.
    Szczeniak, M.W., Makaowska, I.: lncRNA-RNA interactions across the human transcriptome. PloS One 11(3), e0150353 (2016)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Gerlach, W., Giegerich, R.: GUUGle: a utility for fast exact matching under RNA complementary rules including GU base pairing. Bioinformatics 22(6), 762–764 (2006)CrossRefGoogle Scholar
  7. 7.
    Kato, Y. et al.: RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming. Bioinformatics 26(18), i460–i466 (2010)CrossRefGoogle Scholar
  8. 8.
    Li, J. et al.: LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Brief. Bioinfo. 16(5), 806–812 (2014)CrossRefGoogle Scholar
  9. 9.
    Fukunaga, T., Hamada, M.: RIblast: an ultrafast RNA RNA interaction prediction system based on a seed-and-extension approach. Bioinformatics (2017)Google Scholar
  10. 10.
    Wenzel, A., Akbali, E., Gorodkin, J.: RIsearch: fast RNA RNA interaction search using a simplified nearest-neighbor energy model. Bioinformatics 28(21), 2738–2746 (2012)CrossRefGoogle Scholar
  11. 11.
    Hofacker, I.L.: RNA secondary structure analysis using the Vienna RNA package. In: Current Protocols in Bioinformatics, pp. 12–22 (2009)Google Scholar
  12. 12.
  13. 13.
    Kawaguchi, R., Kiryu, H.: Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome. BMC Bioinfo. 17(1), 203 (2016)Google Scholar
  14. 14.
    Fukunaga, T., Ozaki, H., Terai, G., Asai, K., Iwasaki, W., Kiryu, H.: CAPR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol. 15(1), 16 (2014)CrossRefGoogle Scholar
  15. 15.
    Hamada, M., Kiryu, H., Sato, K., Mituyama, T., Asai, K.: Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics 25(4), 46573 (2009)CrossRefGoogle Scholar
  16. 16.
    Kiryu, H. et al.: A detailed investigation of accessibilities around target sites of siRNAs and miRNAs. Bioinformatics 27(13), 1788–1797 (2011)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Panwar, B., Amit, A., Gajendra, P.S.R.: Prediction and classification of ncRNAs using structural information. BMC Genom. 15(1), 127 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Central University of RajasthanAjmerIndia

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