Eur. J. Entomol. 110 (1): 13-20, 2013 | DOI: 10.14411/eje.2013.002

Bioinformatics analysis on structural features of microRNA precursors in insects

Jisheng LI1,2, Wei FAN1, Zhengying YOU1, Boxiong ZHONG1
1 College of Animal Sciences, Zijingang Campus, Zhejiang University, Hangzhou 310058, P.R. China
2 Institute of Sericulture, Chengde Medical University, Chengde 067000, P.R. China

To date, thousands of microRNAs (miRNAs) and their precursors (pre-miRNAs) have been identified in insects and their nucleotide sequences deposited in the miRBase database. In the present work, we have systematically analyzed, utilizing bioinformatics tools, the featural differences between human and insect pre-miRNAs, as well as differences across 24 insect species. Results showed that the nucleotide composition, sequence length, nucleotides preference and secondary structure features between human and insects were different. Subsequently, with the aid of three available SVM-based prediction programs, pre-miRNA sequences were evaluated and given corresponding scores. Thus it was found that of 2633 sequences from the 24 chosen insect species, 2229 (84.7%) were successfully recognized by the Mirident classifier, higher than Triplet-SVM (72.5%) and PMirP (72.6%). In contrast, four species, including the domesticated silkworm, Bombyx mori L., the fruit fly, Drosophila melanogaster Meigen, the honeybee, Apis mellifera L. and the red flour beetle, Tribolium castaneum (Herbst), were found to be largely responsible for the poor performance of some sequence matching. Compared with other species, B. mori especially showed the worst performance with the lowest average MFE index (0.73). Collectively these results pave the way for understanding specificity and diversity of miRNA precursors in insects, and lay the foundation for the further development of more suitable algorisms for insects.

Keywords: Structural features, bioinformatics, insects, microRNA precursors

Received: July 4, 2012; Accepted: August 19, 2012; Published: January 2, 2013

Download citation

References

  1. AMBROS V. 2004: The functions of animal microRNAs. - Nature 431: 350-355 Go to original source...
  2. AMBROS V., BARTEL B., BARTEL D.P., BURGE C.B., CARRINGTON J.C., CHEN X., DREYFUSS G., EDDY S.R., GRIFFITHS-JONES S., MARSHALL M., MATZKE M., RUVKUN G. & TUSCHL T. 2003: A uniform system for microRNA annotation. - RNA 9: 277-279 Go to original source...
  3. BARTEL D.P. 2004: MicroRNAs: genomics, biogenesis, mechanism, and function. - Cell 116: 281-297 Go to original source...
  4. BENTWICH I., AVNIEL A., KAROV Y., AHARONOV R., GILAD S., BARAD O., BARZILAI A., EINAT P., EINAV U., MEIRI E., SHARON E., SPECTOR Y. & BENTWICH Z. 2005: Identification of hundreds of conserved and nonconserved human microRNAs. - Nat. Genet. 37: 766-770 Go to original source...
  5. BUSHATI N. & COHEN S.M. 2007: MicroRNA functions. - Annu. Rev. Cell Dev. Biol. 23: 175-205 Go to original source...
  6. CHANG C.C. & LIN C.J. 2011: LIBSVM: a library for support vector machines. - ACM Trans. Intell. Syst. Technol. 2: 27 Go to original source...
  7. DONG Q.H., HAN J., YU H.P., WANG C., ZHAO M.Z., LIU H., GE A.J. & FANG J.G. 2012: Computational identification of microRNAs in strawberry expressed sequence tags and validation of their precise sequences by miR-RACE. - J. Heredity 103: 268-277 Go to original source...
  8. FREIER S.M., KIERZEK R., JAEGER J.A., SUGIMOTO N., CARUTHERS M.H., NEILSON T. & TURNER D.H. 1986: Improved free-energy parameters for predictions of RNA duplex stability. - Proc. Natl. Acad. Sci. U.S.A. 83: 9373-9377 Go to original source...
  9. GESELLCHEN V. & BOUTROS M. 2004: Managing the genome: microRNAs in Drosophila. - Differentiation 72: 74-80 Go to original source...
  10. GRIFFITHS-JONES S. 2004: The microRNA registry. - Nucl. Acids Res. 32: D109-111 Go to original source...
  11. HOFACKER I.L. 2003: Vienna RNA secondary structure server. - Nucl. Acids Res. 31: 3429-3431 Go to original source...
  12. HOFACKER I.L., FONTANA W., STADLER P.F., BONHOEFFER L.S., TACKER M. & SCHUSTER P. 1994: Fast folding and comparison of RNA secondary structures. - Mh. Chem. / Chem. Month. 125: 167-188 Go to original source...
  13. JIANG P., WU H., WANG W., MA W., SUN X. & LU Z. 2007: MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. - Nucl. Acids Res. 35: W339-W344 Go to original source...
  14. JONES-RHOADES M.W. & BARTEL D.P. 2004: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. - Mol. Cell 14: 787-799 Go to original source...
  15. KOZOMARA A. & GRIFFITHS-JONES S. 2011: miRBase: integrating microRNA annotation and deep-sequencing data. - Nucl. Acids Res. 39: D152-157 Go to original source...
  16. LAI E.C., TOMANCAK P., WILLIAMS R.W. & RUBIN G.M. 2003: Computational identification of Drosophila microRNA genes. - Genome Biol. 4: R42 Go to original source...
  17. LEE R.C., FEINBAUM R.L. & AMBROS V. 1993: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. - Cell 75: 843-854 Go to original source...
  18. LEE Y., AHN C., HAN J., CHOI H., KIM J., YIM J., LEE J., PROVOST P., RADMARK O., KIM S. & KIM V.N. 2003: The nuclear RNase III Drosha initiates microRNA processing. - Nature 425: 415-419 Go to original source...
  19. LEE Y., KIM M., HAN J., YEOM K.H., LEE S., BAEK S.H. & KIM V.N. 2004: MicroRNA genes are transcribed by RNA polymerase II. - EMBO J. 23: 4051-4060 Go to original source...
  20. LI L., XU J., YANG D., TAN X. & WANG H. 2010: Computational approaches for microRNA studies: a review. - Mamm. Genome 21: 1-12 Go to original source...
  21. LIM L.P., GLASNER M.E., YEKTA S., BURGE C.B. & BARTEL D.P. 2003a: Vertebrate microRNA genes. - Science 299: 1540 Go to original source...
  22. LIM L.P., LAU N.C., WEINSTEIN E.G., ABDELHAKIM A., YEKTA S., RHOADES M.W., BURGE C.B. & BARTEL D.P. 2003b: The microRNAs of Caenorhabditis elegans. - Genes Dev. 17: 991-1008 Go to original source...
  23. LIU X., HE S., SKOGERBO G., GONG F. & CHEN R. 2012: Integrated sequence-structure motifs suffice to identify microRNA precursors. PLoS One 7: e32797 Go to original source...
  24. MATHEWS D.H., SABINA J., ZUKER M. & TURNER D.H. 1999: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. - J. Mol. Biol. 288: 911-940 Go to original source...
  25. NAM J.W., SHIN K.R., HAN J., LEE Y., KIM V.N. & ZHANG B.T. 2005: Human microRNA prediction through a probabilistic co-learning model of sequence and structure. - Nucl. Acids Res. 33: 3570-3581 Go to original source...
  26. SEFFENS W. & DIGBY D. 1999: mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. - Nucl. Acids Res. 27: 1578-1584 Go to original source...
  27. UNVER T., NAMUTH-COVERT D.M. & BUDAK H. 2009: Review of current methodological approaches for characterizing microRNAs in plants. - Int. J. Plant Genomics 2009: 262463 Go to original source...
  28. WANG Q.L. & LI Z.H. 2007: The functions of microRNAs in plants. - Front Biosci. 12: 3975-3982
  29. WANG X., ZHANG J., LI F., GU J., HE T., ZHANG X. & LI Y. 2005: MicroRNA identification based on sequence and structure alignment. - Bioinformatics 21: 3610-3614 Go to original source...
  30. WANG Y., STRICKER H.M., GOU D. & LIU L. 2007: MicroRNA: past and present. - Front Biosci. 12: 2316-2329 Go to original source...
  31. XUE C., LI F., HE T., LIU G.P., LI Y. & ZHANG X. 2005: Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. - BMC Bioinform. 6: 310 Go to original source...
  32. ZHANG B., PAN X., COX S., COBB G. & ANDERSON T. 2006: Evidence that miRNAs are different from other RNAs. - Cell. Mol. Life Sci. 63: 246-254 Go to original source...
  33. ZHAO D., WANG Y., LUO D., SHI X., WANG L., XU D., YU J. & LIANG Y. 2010: PMirP: A pre-microRNA prediction method based on structure-sequence hybrid features. - Artificial Intell. Medicine 49: 127-132 Go to original source...
  34. ZUKER M. 2003: Mfold web server for nucleic acid folding and hybridization prediction. - Nucl. Acids Res. 31: 3406-3415 Go to original source...