5th International Virtual Conference (IVC-2018).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Comparison of RNA Secondary Structure Prediction Tools in Predicting the Structure

Author Affiliations

  • 1 Department of Microbiology, Shree Ramkrishna Institute of Computer Education and Applied Sciences, Athwalines, Surat-395001, Gujarat, INDIA

Res. J. Recent Sci., Volume 3, Issue (IVC-2014), Pages 20-23, (2014)

Abstract

Many numbers of software applications (GUIs) are available for the single stranded nucleic acid secondary structure prediction-like Mfold, CONTRA fold, IPknot, Compa RNA, Centroid Alifold, etc. Some uses Minimum Free Energy models (MFE) algorithm and others use stochastic context-free grammars (SCFGs), and rest rely on dynamic programming evolved as an alternative probabilistic methodology for modelling RNA structure. In contrast to physics-based methods, which are dependent on thousands of experimentally-measured thermodynamic parameters, SCFGs require fully-automated statistical learning algorithms to derive model parameters. The performance of 10 single-sequences from a numerous RNA sequences with respective methods were being evaluated. On the whole the most accurate and stable predictions obtained by single-sequence analyses are generated by Mfold, IPknot, RNA Structure and COFOLD.

References

  1. Eddy S.R., Noncoding RNA genes and the modern RNA world, Nat. Rev. Genet., 2, 919929 (2001)
  2. Huttenhofer A. and Schattner P., The principles of guiding by RNA: Chimeric RNA-protein enzymes, Nat. Rev. Genet, 7, 475482 (2006)
  3. Doudna J.A. and Cech T.R., The chemical repertoire ofnatural ribozymes, Nature, 418, 222228 (2002)
  4. Bachellerie J.P., Cavaille J. and Huttenhofer A., Theexpanding snoRNA world, Biochimie, 84, 775790(2002)
  5. Gong C. and Maquat L.E., lnc RNAs transactivate STAU1-mediated mRNA decay by duplexing with 3 UTRs via Alu elements, Nature, 470, 284288 (2011)
  6. Sucheck, S.J. and Wong, C.H. RNA as a target for small molecules, Curr. Opin. Chem. Biol., 4, 678686 (2000)
  7. Guan L. and Disney M.D., Recent advances in developing small molecules targeting RNA, ACS Chem. Biol., 7, 7386(2012)
  8. Mathews D., Sabina J., Zuker M. and Turner D., Expanded sequence dependence of thermodynamicparameters improves prediction of RNA secondary structure, J Mol Biol, 288(5), 911940 (1999)
  9. Li X., Quon G., Lipshitz H.D. and Morris Q., Predicting in vivo binding sites of RNA-binding proteins using mRNA secondary structure, RNA, 16, 10961107 (2010)
  10. Zuker M., On finding all suboptimal foldings of an RNA molecule, Science, 244, 4852 (1989)
  11. Duan S., Mathews D.H. and Turner D.H., Interpreting oligonucleotide microarray data to determine RNA secondary structure: application to the 3 end of Bombyx mori R2 RNA, Biochemistry, 45, 98199832 (2006)
  12. Wuchty S., Fontana W., Hofacker I.L. and Schuster P., Complete suboptimal folding of RNA and the stability of secondary structures, Biopolymers, 49, 145165 (1999)
  13. Reuter J.S. and Mathews D.H., RNA structure: software for RNA secondary structure prediction and analysis, BMC Bioinformatics, 11, 129 (2010)
  14. Lu Z.J., Gloor J.W. and Mathews D.H., Improved RNA secondary structure prediction by maximizing expected pair accuracy, RNA, 15, 18051813 (2009)
  15. Piekna-Przybylska D., DiChiacchio L., Mathews D.H. and Bambara R.A., A sequence similar to tRNA3Lys gene is embedded in HIV-1 U3/R and pro promotes minus strand transfer, Nat. Struct. Mol. Biol., 17, 8389 (2009)
  16. Mathews D.H., Using an RNA secondary structure partition functions to determine confidence in base pairs predicted by free energy minimization, RNA, 10, 11781190 (2004)