A hybrid clustering/evolutionary algorithm for RNA folding

Wiese, Kay C.; Hendriks, A.

doi:10.1109/cibcb.2008.4675754

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…These differences approached only 0.011 for all statistics when 5S rRNA sequences were excluded from the comparison. Future work will investigate the use of clustering techniques [18] to further improve structure prediction quality.…”

Section: Discussionmentioning

confidence: 99%

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Expanded study of efn2 thermodynamic model performance on RnaPredict, an evolutionary algorithm for RNA folding

Wiese

Hendriks

2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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The shape that organic molecules such as biopolymers form within organic systems largely determines the function said molecules perform. RNA is a biopolymer that plays a central part in several stages of protein synthesis, and also has structural, functional, and regulatory roles in the cell. In an ab initio case most common structure prediction techniques employ minimization of the free energy of a given RNA molecule via a thermodynamic model. RnaPredict is an evolutionary algorithm for RNA folding. This paper compares the performance of an advanced thermodynamic model, efn2, against the stacking-energy thermodynamic models INN and INN-HB on a test set containing 24 sequences from 4 rRNA subtypes. The prediction accuracy of efn2 is demonstrated on a majority of test sequences. A comparison is also made with the mfold prediction algorithm which demonstrated RnaPredict's comparable performance.

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Section: Discussionmentioning

confidence: 99%

“…Algorithm performance was compared to the Nussinov DP algorithm [16] in [17]. Recent research includes the creation of a hybrid algorithm through the combination of RnaPredict with a clustering algorithm [18].…”

mentioning

confidence: 99%

Expanded study of efn2 thermodynamic model performance on RnaPredict, an evolutionary algorithm for RNA folding

Wiese

Hendriks

2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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“…There is a large literature on RNA secondary structure estimation based on primary sequence [10,11]. Much of this literature uses the results of laboratory thermodynamic studies of RNA as its basis.…”

Section: Introductionmentioning

confidence: 99%

Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

Smith

2010

Pattern Recognition in Bioinformatics

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Abstract. The effect of more detailed modeling of the interface between stem and loop in non-coding RNA hairpin structures on efficacy of covariance-model-based non-coding RNA gene search is examined. Currently, the prior probabilities of the two stem nucleotides and two loop-end nucleotides at the interface are treated the same as any other stem and loop nucleotides respectively. Laboratory thermodynamic studies show that hairpin stability is dependent on the identities of these four nucleotides, but this is not taken into account in current covariance models. It is shown that separate estimation of emission priors for these nucleotides and joint treatment of substitution probabilities for the two loop-end nucleotides leads to improved non-coding RNA gene search.Keywords: sequence analysis, RNA gene search, covariance models IntroductionCovariance models are an effective method of capturing the joint probability information inherent in the intramolecularly base-paired positions of a non-coding RNA molecule [1,2]. Unlike profile hidden Markov models [3,4], which have a set of four emission probabilities over the possible nucleotides at each consensus sequence position, covariance models allow consensus base pairs to be assigned sixteen joint probabilities over the possible ordered nucleotide pairs. Covariance models also allow the probability of insertion or deletion of a base pair to be different than the sum of the marginal probabilities of insertion or deletion of the individual nucleotides. The profile hidden Markov model can be viewed as a special form of a covariance model with no base pairs specified.

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Impact of an enhanced thermodynamic model on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction

Wiese

Hendriks

2009

2009 IEEE Congress on Evolutionary Computation

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RNA has important structural, functional, and regulatory parts in the cell as well as a critical role in multiple stages of protein synthesis. An RNA molecule's shape largely determines its function in an organic system. Accordingly, computational RNA structural prediction methods are of significant interest. For ab initio cases where only an RNA sequence is known, structure prediction techniques typically employ free energy minimization of a given RNA molecule via a thermodynamic model. Unfortunately, the minimum free energy structure is rarely the native structure. This is thought to be due to errors in the experimentally determined thermodynamic model parameters.RnaPredict is an evolutionary algorithm designed for the prediction of RNA secondary structure; it currently utilizes the stacking-energy thermodynamic models INN and INN-HB. The effect of an enhanced model, efn2, on RnaPredict is investigated. The efn2 model significantly improved the sensitivity and specificity of the majority of structures evaluated.

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A hybrid clustering/evolutionary algorithm for RNA folding

Cited by 3 publications

References 23 publications

Expanded study of efn2 thermodynamic model performance on RnaPredict, an evolutionary algorithm for RNA folding

Expanded study of efn2 thermodynamic model performance on RnaPredict, an evolutionary algorithm for RNA folding

Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

Impact of an enhanced thermodynamic model on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction

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