Analysis and Classification of Plant MicroRNAs Using Decision Tree Based Approach

  • A. K Mishra
  • H. Chandrasekharan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


MicroRNA (miRNA) analysis have progressed tremendously in recent past, but further indepth computational study is required to know the complete potential of these RNAs. Due to its short length (~20 nucleotides), it is difficult to use the conventional lab techniques for microRNA prediction and analysis. This has led to this work in the domain of computational biology. These are the non coding small RNAs which are responsible for the gene regulation at the post translational level by binding to the mRNAs and thereby stopping the translation activities. Therefore,the effect of microRNAs on the various proteins is important. In this paper we have studied 1010 microRNA and precursor microRNA sequences from monocots . Our study in this paper is on the microRNA classification using decision trees and determining dominating attributes. We have used WEKA, a data mining tool which helps us to study the large data and classify it. The decision trees based classification was best suited for the miRNA study and the dominating attributes derived are biologically significant.


miRBase precursor RNAFold WEKA J48 Decision Trees 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    He, L., Hannon, G.J.: MicroRNAs: small RNAs with a big role in gene regulation. Nature Genetics (2004)Google Scholar
  2. 2.
    Lee, Y., Jeon, K., Lee, J.-T., Kim, S., Kim, V.N.: MicroRNA maturation: stepwise processing and subcellular localization. EMBO J. (2002)Google Scholar
  3. 3.
    Alvarez-Garcia, I., Miska, E.A.: MicroRNA functions in animal development and human disease. Development. The Company of Biologists (2005)Google Scholar
  4. 4.
    Mendes, N.D., Freitas, A.T., Sagot, M.-F.: Current tools for the identification of miRNA genes and their targets. Nucleic Acids Research (May 2009)Google Scholar
  5. 5.
    Wang, X., Zhang, J., Li, F., Gu, J., He, T., Zhang, X., Li, Y.: MicroRNA identification based on sequence and structure alignment. Bioinformatics (2005)Google Scholar
  6. 6.
    Hofacker, I.L., Fontana, W., Stadler, P.F., Bonhoeffer, S., Tacker, M., Schuster, P.: FastFolding and Comparison of RNA Secondary Structures. Monatshefte F. Chemie 125, 167–188 (1994)Google Scholar
  7. 7.
    Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamic and auxiliary information. Nucl. Acid Res. (1981)Google Scholar
  8. 8.
    Hofacker, I.L., Stadler, P.F.: Memory Efficient Folding Algorithms for Circular RNA Secondary Structures. Bioinformatics (2006)Google Scholar
  9. 9.
    Bompfunewerer, A.F., Backofen, R., Bernhart, S.H., Hertel, J., Hofacker, I.L., Stadler, P.F., Will, S.: Variations on Folding and Alignment: Lessons from Benasque. J. Math. Biol. (2007)Google Scholar
  10. 10.
    Mishra, A.K., Lobiyal, D.K.: Exploring Dominating Features from Apis Mellifera Pre- miRNA. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • A. K Mishra
    • 1
  • H. Chandrasekharan
    • 1
  1. 1.Indian Agricultural Research InstituteAKMUNew DelhiIndia

Personalised recommendations