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A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.


ABSTRACT: Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.

SUBMITTER: Layton DM 

PROVIDER: S-EPMC548333 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

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A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.

Layton D M DM   Bundschuh R R  

Nucleic acids research 20050126 2


Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved  ...[more]

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