Literature DB >> 2454711

A program for predicting significant RNA secondary structures.

S V Le1, J H Chen, K M Currey, J V Maizel.   

Abstract

We describe a program for the analysis of RNA secondary structure. There are two new features in this program. (i) To get vector speeds on a vector pipeline machine (such as Cray X-MP/24) we have vectorized the secondary structure dynamic algorithm. (ii) The statistical significance of a locally 'optimal' secondary structure is assessed by a Monte Carlo method. The results can be depicted graphically including profiles of the stability of local secondary structures and the distribution of the potentially significant secondary structures in the RNA molecules. Interesting regions where both the potentially significant secondary structures and 'open' structures (single-stranded coils) occur can be identified by the plots mentioned above. Furthermore, the speed of the vectorized code allows repeated Monte Carlo simulations with different overlapping window sizes. Thus, the optimal size of the significant secondary structure occurring in the interesting region can be assessed by repeating the Monte Carlo simulation. The power of the program is demonstrated in the analysis of local secondary structures of human T-cell lymphotrophic virus type III (HIV).

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Year:  1988        PMID: 2454711     DOI: 10.1093/bioinformatics/4.1.153

Source DB:  PubMed          Journal:  Comput Appl Biosci        ISSN: 0266-7061


  26 in total

1.  A computational approach to identify genes for functional RNAs in genomic sequences.

Authors:  R J Carter; I Dubchak; S R Holbrook
Journal:  Nucleic Acids Res       Date:  2001-10-01       Impact factor: 16.971

2.  A comparative method for finding and folding RNA secondary structures within protein-coding regions.

Authors:  Jakob Skou Pedersen; Irmtraud Margret Meyer; Roald Forsberg; Peter Simmonds; Jotun Hein
Journal:  Nucleic Acids Res       Date:  2004-09-24       Impact factor: 16.971

Review 3.  Folding and finding RNA secondary structure.

Authors:  David H Mathews; Walter N Moss; Douglas H Turner
Journal:  Cold Spring Harb Perspect Biol       Date:  2010-08-04       Impact factor: 10.005

4.  Fast and reliable prediction of noncoding RNAs.

Authors:  Stefan Washietl; Ivo L Hofacker; Peter F Stadler
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-21       Impact factor: 11.205

5.  Tracking down noncoding RNAs.

Authors:  Vincent Moulton
Journal:  Proc Natl Acad Sci U S A       Date:  2005-02-09       Impact factor: 11.205

6.  Evolutionary patterns of non-coding RNAs.

Authors:  Athanasius F Bompfünewerer; Christoph Flamm; Claudia Fried; Guido Fritzsch; Ivo L Hofacker; Jörg Lehmann; Kristin Missal; Axel Mosig; Bettina Müller; Sonja J Prohaska; Bärbel M R Stadler; Peter F Stadler; Andrea Tanzer; Stefan Washietl; Christina Witwer
Journal:  Theory Biosci       Date:  2005-04       Impact factor: 1.919

Review 7.  Computational methods in noncoding RNA research.

Authors:  Ariane Machado-Lima; Hernando A del Portillo; Alan Mitchell Durham
Journal:  J Math Biol       Date:  2007-09-04       Impact factor: 2.259

8.  Prediction of alternative RNA secondary structures based on fluctuating thermodynamic parameters.

Authors:  S Y Le; J H Chen; J V Maizel
Journal:  Nucleic Acids Res       Date:  1993-05-11       Impact factor: 16.971

9.  "Well-determined" regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA.

Authors:  M Zuker; A B Jacobson
Journal:  Nucleic Acids Res       Date:  1995-07-25       Impact factor: 16.971

10.  Automatic detection of conserved RNA structure elements in complete RNA virus genomes.

Authors:  I L Hofacker; M Fekete; C Flamm; M A Huynen; S Rauscher; P E Stolorz; P F Stadler
Journal:  Nucleic Acids Res       Date:  1998-08-15       Impact factor: 16.971

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