Literature DB >> 11902836

Dynalign: an algorithm for finding the secondary structure common to two RNA sequences.

David H Mathews1, Douglas H Turner.   

Abstract

With the rapid increase in the size of the genome sequence database, computational analysis of RNA will become increasingly important in revealing structure-function relationships and potential drug targets. RNA secondary structure prediction for a single sequence is 73 % accurate on average for a large database of known secondary structures. This level of accuracy provides a good starting point for determining a secondary structure either by comparative sequence analysis or by the interpretation of experimental studies. Dynalign is a new computer algorithm that improves the accuracy of structure prediction by combining free energy minimization and comparative sequence analysis to find a low free energy structure common to two sequences without requiring any sequence identity. It uses a dynamic programming construct suggested by Sankoff. Dynalign, however, restricts the maximum distance, M, allowed between aligned nucleotides in the two sequences. This makes the calculation tractable because the complexity is simplified to O(M(3)N(3)), where N is the length of the shorter sequence. The accuracy of Dynalign was tested with sets of 13 tRNAs, seven 5 S rRNAs, and two R2 3' UTR sequences. On average, Dynalign predicted 86.1 % of known base-pairs in the tRNAs, as compared to 59.7 % for free energy minimization alone. For the 5 S rRNAs, the average accuracy improves from 47.8 % to 86.4 %. The secondary structure of the R2 3' UTR from Drosophila takahashii is poorly predicted by standard free energy minimization. With Dynalign, however, the structure predicted in tandem with the sequence from Drosophila melanogaster nearly matches the structure determined by comparative sequence analysis. Copyright 2002 Elsevier Science Ltd.

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Year:  2002        PMID: 11902836     DOI: 10.1006/jmbi.2001.5351

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  144 in total

1.  ILM: a web server for predicting RNA secondary structures with pseudoknots.

Authors:  Jianhua Ruan; Gary D Stormo; Weixiong Zhang
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

2.  MSARI: multiple sequence alignments for statistical detection of RNA secondary structure.

Authors:  Alex Coventry; Daniel J Kleitman; Bonnie Berger
Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-10       Impact factor: 11.205

3.  Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization.

Authors:  David H Mathews
Journal:  RNA       Date:  2004-08       Impact factor: 4.942

4.  BayesFold: rational 2 degrees folds that combine thermodynamic, covariation, and chemical data for aligned RNA sequences.

Authors:  Rob Knight; Amanda Birmingham; Michael Yarus
Journal:  RNA       Date:  2004-09       Impact factor: 4.942

5.  A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

Authors:  Elena Rivas; Raymond Lang; Sean R Eddy
Journal:  RNA       Date:  2011-12-22       Impact factor: 4.942

6.  TurboKnot: rapid prediction of conserved RNA secondary structures including pseudoknots.

Authors:  Matthew G Seetin; David H Mathews
Journal:  Bioinformatics       Date:  2012-01-27       Impact factor: 6.937

7.  LocARNA-P: accurate boundary prediction and improved detection of structural RNAs.

Authors:  Sebastian Will; Tejal Joshi; Ivo L Hofacker; Peter F Stadler; Rolf Backofen
Journal:  RNA       Date:  2012-03-26       Impact factor: 4.942

Review 8.  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

9.  Multilign: an algorithm to predict secondary structures conserved in multiple RNA sequences.

Authors:  Zhenjiang Xu; David H Mathews
Journal:  Bioinformatics       Date:  2010-12-30       Impact factor: 6.937

10.  RNA challenges for computational chemists.

Authors:  Ilyas Yildirim; Douglas H Turner
Journal:  Biochemistry       Date:  2005-10-11       Impact factor: 3.162

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