A probabilistic approach to microRNA-target binding

Biochemical and Biophysical Research Communications

Volumn 413, Issue 1, 2011, Pages 111-115

  Oĝul, Hasan ^a   Umu, Sinan U ^b   Tuncel, Y Yener ^b   Akkaya, Mahinur S ^b  

^a BASKENT UNIVERSITY   (Turkey)

^b MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

MicroRNA target duplex; Probabilistic sequence model; Variable Length Markov Chains

Indexed keywords

MESSENGER RNA; MICRORNA;

ACCURACY; ARTICLE; BASE PAIRING; BINDING SITE; GENE CONTROL; GENE TARGETING; MOLECULAR INTERACTION; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; REGULATORY MECHANISM; RNA BINDING; RNA SEQUENCE; THERMODYNAMICS;

COMPUTER SIMULATION; MICRORNAS; MODELS, CHEMICAL; PROBABILITY; RNA, MESSENGER;

EID: 80052851826     PISSN: 0006291X     EISSN: 10902104     Source Type: Journal    
DOI: 10.1016/j.bbrc.2011.08.065     Document Type: Article

References (30)

1. Lee R.C., Feinbaum R.L., Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75:843-854.
2. Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116:281-297.
3. Alexiou P., Maragkakis M., Papadopoulos G.L., et al. Lost in translation: an assessment and perspective for computational microRNA target identification. Bioinformatics 2009, 25:3049-3055.
4. Szabo P.M., Tombol Z., Molnar V., et al. MicroRNA target prediction: problems and possible solutions. Curr. Bioinf. 2010, 5:81-88.
5. Hammell M. Computational methods to identify miRNA targets. Semin. Cell Dev. Biol. 2010, 21:738-744.
6. Bartel D.P. MicroRNAs: target recognition and regulatory functions. Cell 2009, 136:215-233.
7. Yoon S., Micheli G. Prediction of regulatory modules comprising microRNAs and target genes. Bioinformatics 2005, 21:i93-i100.
8. Liu B., Liu L., Tsykin A., et al. Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation. Bioinformatics 2010, 26:3105-3111.
9. Lu J., Getz G., Miska E.A., et al. MicroRNA expression profiles classify human cancers. Nature 2005, 435:834-838.
10. Madden S.F., Carpenter S.B., Jeffery I.B., et al. Detecting microRNA activity from gene expression data. BMC Bioinf. 2010, 11:257.
11. Yu G., He Q. Functional similarity analysis of human virus-encoded miRNAs. J. Clin. Bioinf. 2011, 1:1-15.
12. Wang D., Wang J., Lu M., et al. Bioinformatics 2010, 26:1644-1650.
13. Barbato C., Arisi I., Frizzo M.E., et al. Computational challenges in miRNA target predictions: to be or not to be a true target?. J. Biomed. Biotechnol. 2009, 2009:803069.
14. Krek A., Grun D., Poy M.N., et al. Combinatorial microRNA target predictions. Nat. Genet. 2005, 37:495-500.
15. Huang J.C., Morris Q.D., Frey B.J. Bayesian inference of microRNA targets from sequence and expression data. J. Comput. Biol. 2007, 14:550-563.
16. Ron D., Singer Y., Tishby N. The power of amnesia: learning probabilistic automata with variable memory length. Mach. Learn. 1996, 25:117-149.
17. Yang Y., Wang Y.P., Li K.B. miRTif: a support vector machine-based microRNA target interaction filter. BMC Bioinf. 2008, 9:S4.
18. Xiao F., Zuo Z., Cai G., et al. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009, 37:D105-D110.
19. Papadopoulos G.L., Reczko M., Simossis V.A., et al. The database of experimentally supported targets: a functional update of tarbase. Nucleic Acids Res. 2009, 37:D155-D158.
20. Smith T., Waterman M. Identification of common molecular subsequences. J. Mol. Biol. 1981, 147:195-197.
21. Thijs G., Lescot M., Marchal K., et al. A higher order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 2001, 17:1113-1122.
22. Bejerano G., Yona G. Variations on probabilistic suffix trees: statistical modeling and prediction of protein families. Bioinformatics 2001, 17:23-43.
23. Ogul H. Variable context Markov chains for HIV protease cleavage site prediction. Biosystems 2009, 96:246-250.
24. Ben-Gal I., Shani A., Gohr A., et al. Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics 2004, 21:2657-2666.
25. Leonardi F.G. A generalization of the pst algorithm: modeling the sparse nature of protein sequences. Bioinformatics 2006, 22:1302-1307.
26. Rehmsmeier M., Steffen P., Hochmann M., et al. Fast and effective prediction of microRNA/target duplexes. RNA 2004, 10:1507-1517.
27. Chen X. MicroRNA metabolism in plants. Curr. Top. Microbiol. 2008, 320:117-136.
28. Hu H.Y., Yan Z., Xu Y., et al. Sequence features associated with microRNA strand selection in humans and flies. BMC Genomics 2009, 10:413.
29. Eamens A.L., Smith N.A., Curtin S.J., et al. The Arabidopsis thaliana double-stranded RNA binding protein DRB1 directs guide strand selection from microRNA duplexes. RNA 2009, 15:2219-2235.
30. Brodersen P., Voinnet O. Revisiting the principles of microRNA target recognition and mode of action. Nat. Rev. Mol. Cell Biol. 2009, 10:141-148.