miRNA controls many cellular processes, it is important to identify their targets with high accuracy. In this work, we propose a systematic method of identifying miRNA targets. Users should provide the expression profiles of the specific miRNA for us to identify a group of potential miRNA target genes. In this approach, we can observe the reduction of mRNA level, not just the amount of protein deriving from mRNA. Then three common used computational prediction tools were integrated for finding miRNA targets. To increase the accuracy of miRNA target prediction, we observed the experimentally tested miRNA target sites and developed several criteria. Moreover, we also provided the expression profiles of both miRNA and its target gene to describe miRNA/target relationship. Finally, in this work, we concentrate the miRNA target identification in human genome. However, this systematic approach is suitable for each species but with different parameters.
Reference
1. Lau, N.C., et al., An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science, 2001. 294(5543): p. 858-62.
2. Lee, R.C. and V. Ambros, An extensive class of small RNAs in Caenorhabditis elegans. Science, 2001. 294(5543): p. 862-4.
3. Lagos-Quintana, M., et al., Identification of novel genes coding for small expressed RNAs. Science, 2001. 294(5543): p. 853-8.
4. He, L. and G.J. Hannon, MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet, 2004. 5(7): p. 522-31.
5. Girard, A., et al., A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature, 2006. 442(7099): p. 199-202.
6. Aravin, A., et al., A novel class of small RNAs bind to MILI protein in mouse testes. Nature, 2006. 442(7099): p. 203-7.
7. Kim, V.N., Small RNAs just got bigger: Piwi-interacting RNAs (piRNAs) in mammalian testes. Genes Dev, 2006. 20(15): p. 1993-7.
8. Chapman, E.J. and J.C. Carrington, Specialization and evolution of endogenous small RNA pathways. Nat Rev Genet, 2007. 8(11): p. 884-96.
9. Lee, R.C., R.L. Feinbaum, and V. Ambros, The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993.
75(5): p. 843-54.
10. Ha, I., B. Wightman, and G. Ruvkun, A bulged lin-4/lin-14 RNA duplex is sufficient for Caenorhabditis elegans lin-14 temporal gradient formation. Genes Dev, 1996. 10(23): p. 3041-50.
11. Reinhart, B.J., et al., The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 2000. 403(6772): p. 901-6.
12. Kosik, K.S., The neuronal microRNA system. Nat Rev Neurosci, 2006. 7(12): p.
911-20.
13. Wienholds, E. and R.H. Plasterk, MicroRNA function in animal development.
FEBS Lett, 2005. 579(26): p. 5911-22.
14. Rhoades, M.W., et al., Prediction of plant microRNA targets. Cell, 2002. 110(4):
p. 513-20.
15. Lewis, B.P., et al., Prediction of mammalian microRNA targets. Cell, 2003.
115(7): p. 787-98.
16. Lewis, B.P., C.B. Burge, and D.P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.
Cell, 2005. 120(1): p. 15-20.
17. Enright, A.J., et al., MicroRNA targets in Drosophila. Genome Biol, 2003. 5(1):
p. R1.
18. Rehmsmeier, M., et al., Fast and effective prediction of microRNA/target duplexes. Rna, 2004. 10(10): p. 1507-17.
19. Kruger, J. and M. Rehmsmeier, RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res, 2006. 34(Web Server issue): p. W451-4.
20. Sethupathy, P., B. Corda, and A.G. Hatzigeorgiou, TarBase: A comprehensive database of experimentally supported animal microRNA targets. Rna, 2006.
12(2): p. 192-7.
21. Griffiths-Jones, S., et al., miRBase: tools for microRNA genomics. Nucleic Acids Res, 2008. 36(Database issue): p. D154-8.
22. Griffiths-Jones, S., et al., miRBase: microRNA sequences, targets and gene
nomenclature. Nucleic Acids Res, 2006. 34(Database issue): p. D140-4.
23. Hsu, P.W., et al., miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res, 2006. 34(Database issue): p.
D135-9.
24. Hsu, S.D., et al., miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Res, 2008. 36(Database issue): p. D165-9.
25. Nam, S., et al., miRGator: an integrated system for functional annotation of microRNAs. Nucleic Acids Res, 2008. 36(Database issue): p. D159-64.
26. Betel, D., et al., The microRNA.org resource: targets and expression. Nucleic Acids Res, 2008. 36(Database issue): p. D149-53.
27. Kim, S.K., et al., miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics, 2006. 7: p. 411.
28. Hofacker, I.L., RNA secondary structure analysis using the Vienna RNA package.
Curr Protoc Bioinformatics, 2004. Chapter 12: p. Unit 12 2.
29. Hofacker, I.L., Vienna RNA secondary structure server. Nucleic Acids Res, 2003.
31(13): p. 3429-31.
30. Krek, A., et al., Combinatorial microRNA target predictions. Nat Genet, 2005.
37(5): p. 495-500.
31. Kiriakidou, M., et al., A combined computational-experimental approach predicts human microRNA targets. Genes Dev, 2004. 18(10): p. 1165-78.
32. Wuchty, S., et al., Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers, 1999. 49(2): p. 145-65.
33. Wang, X., Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Res, 2006. 34(5): p. 1646-52.
34. Flicek, P., et al., Ensembl 2008. Nucleic Acids Res, 2008. 36(Database issue): p.
D707-14.
35. Barrett, T., et al., NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res, 2007. 35(Database issue): p. D760-5.
36. Ding, Y. and C.E. Lawrence, A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res, 2003. 31(24): p. 7280-301.
37. Stark, A., et al., Identification of Drosophila MicroRNA targets. PLoS Biol, 2003.
1(3): p. E60.
38. Mangan, M.E., et al., UCSC Genome Browser: Deep support for molecular biomedical research. Biotechnol Annu Rev, 2008. 14: p. 63-108.
39. Ding, Y. and C.E. Lawrence, Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond. Nucleic Acids Res, 2001. 29(5): p. 1034-46.
40. Lu, J., et al., MicroRNA expression profiles classify human cancers. Nature, 2005. 435(7043): p. 834-8.
41. Su, A.I., et al., A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A, 2004. 101(16): p. 6062-7.
42. Johnson, S.M., et al., RAS is regulated by the let-7 microRNA family. Cell, 2005.
120(5): p. 635-47.
43. Yekta, S., I.H. Shih, and D.P. Bartel, MicroRNA-directed cleavage of HOXB8 mRNA. Science, 2004. 304(5670): p. 594-6.
44. Gaidatzis, D., et al., Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics, 2007. 8: p. 69.
45. Grimson, A., et al., MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell, 2007. 27(1): p. 91-105.
46. Kertesz, M., et al., The role of site accessibility in microRNA target recognition.
Nat Genet, 2007. 39(10): p. 1278-84.
47. Long, D., et al., Potent effect of target structure on microRNA function. Nat Struct Mol Biol, 2007. 14(4): p. 287-94.
48. Lagos-Quintana, M., et al., Identification of tissue-specific microRNAs from mouse. Curr Biol, 2002. 12(9): p. 735-9.
49. Lim, L.P., et al., Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 2005. 433(7027): p. 769-73.