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miRHunter: A tool for predicting microRNA precursors based on combined computational method

Abstract

MicroRNAs (miRNAs) are small endogenous non-coding RNAs known to post-transcriptionally regulate gene expression in a broad range of organism. Since the discovery of the very first miRNAs, lin-4 and let-7, computational methods have been indispensable tools that complement experimental approaches to understand the biology of miRNAs. In this article, we introduce a web-based computational tool, miRHunter, that identifies potential miRNA precursors (pre-miRNAs) in the genomic sequences by using a combined computational method. The method coupled ab initio method with homology-based and hairpin structure-based methods. The miRHunter consists of five modules: 1) a preprocessing module, 2) an evolutionary conservation filter module, 3) a hairpin structure filter module, 4) a support vector machine module that evaluates preliminary pre-miRNA candidates derived from the previous two filtering modules, and 5) a post-processing module. The miRHunter system yielded the following average test results: 96.16%/93.23%, 96.00%/94.68%, and 95.87%/93.57% which are sensitivity (Sn) and specificity (Sp) for animal, plant, and overall categories respectively. The miRHunter system can complement experimental methods and allow wetlab researchers to screen long sequences for putative miRNAs as well as pre-testing miRNAs of interest. The microarray profiling experiments have supported that the clusters of proximal pairs of miRNAs are generally coexpressed. Therefore, the clustering or spatial localization information will be used to improve the accuracy of our system in further work. The miRHunter is available at http://www.bioinfoworld.com/.

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References

  1. 1.

    Chan, J.A., Krichevsky, A.M. & Kenneth, S.K. MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res. 65, 6029–6033 (2005).

    CAS  Article  Google Scholar 

  2. 2.

    Esquela-Kerscher, A. & Slack, F.J. Oncomirs -microRNAs with a role in cancer. Nat. Rev. Cancer 6, 6:259–269 (2006).

    CAS  Article  Google Scholar 

  3. 3.

    Bartel, D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004).

    CAS  Article  Google Scholar 

  4. 4.

    Yekta, S., Shih, I.H. & Bartel, D.P. MicroRNA-directed cleavage of HOXB8 mRNA. Science 304, 594–596 (2004).

    CAS  Article  Google Scholar 

  5. 5.

    Bagga, S. et al. Regulation by let-7 and lin-4 miRNAs results in target mRNA degradation. Cell 122, 553–563 (2005).

    CAS  Article  Google Scholar 

  6. 6.

    Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Szittya, G. et al. High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics 9, 593. doi: 10.1186/1471-2164-9-593 (2008).

    Google Scholar 

  8. 8.

    Kim, K.B. A survey on computational approaches to the discovery of microRNA genes. Current Bioinformatics 9, 173–181 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    Byvatov, E. & Schneider, G. Support vector machine applications in bioinformatics. Appl. Bioinformatics 2, 67–77 (2003).

    Google Scholar 

  10. 10.

    Lancashire, L.J., Lemetre, C. & Ball, G.R. An introduction to artificial neural networks in bioinformatics -application to complex microarray and mass spectrometry datasets in cancer studies. Brief. Bioinform. 10, 315–329 (2009).

    CAS  Article  Google Scholar 

  11. 11.

    Yoon, B.J. Hidden markov models and their applications in biological sequence analysis. Curr. Genomics 10, 402–415 (2009).

    CAS  Article  Google Scholar 

  12. 12.

    Webb, G.I., Boughton, J. & Wang, Z. Not so Naïve Bayes: aggregating one-dependence estimators. Machine Learning 58, 5–24 (2005).

    Article  Google Scholar 

  13. 13.

    Altschul, S., Gish, W., Miller, W., Myers, E. & Lipman, D. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Article  Google Scholar 

  14. 14.

    Hofacker, I.L. Vienna RNA secondary structure server. Nucleic Acids Res. 31, 3429–h3431 (2003).

    CAS  Article  Google Scholar 

  15. 15.

    Loong, K. & Mishra, S. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics 23, 1321–1330 (2007).

    Article  Google Scholar 

  16. 16.

    Batuwita, R. & Palade, V. microPred: Effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25, 989–995 (2009).

    CAS  Article  Google Scholar 

  17. 17.

    Zhong, Y., Xuan, P., Han, K., Zhang, W. & Li, J. Improved Pre-miRNA Classification by Reducing the Effect of Class Imbalance. Biomed Res. Int. 2015, DOI: 10.1155 (2015).

    Google Scholar 

  18. 18.

    Pruitt, K.D. & Maglott, D.R. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res. 29, 137–140 (2001).

    CAS  Article  Google Scholar 

  19. 19.

    Keerthi, S. & Lin, C.J. Asymptotic behaviours of support vector machines with Gaussian kernel. Neural Comput. 15, 1667–1689 (2003).

    Article  Google Scholar 

  20. 20.

    Chang, C.C. & Lin, C.J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 7:1–27:27 (2011).

    Article  Google Scholar 

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Correspondence to Ki-Bong Kim.

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Koh, I., Kim, KB. miRHunter: A tool for predicting microRNA precursors based on combined computational method. BioChip J 11, 164–171 (2017). https://doi.org/10.1007/s13206-017-1210-3

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Keywords

  • MicroRNAs
  • Gene expression
  • miRHunter
  • Combined computational method
  • Support vector machine