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IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming.

Sato K, Kato Y, Hamada M, Akutsu T, Asai K - Bioinformatics (2011)

Bottom Line: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes.We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given.IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp

ABSTRACT

Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy.

Results: We propose IPknot, a novel computational method for predicting RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure. IPknot decomposes a pseudoknotted structure into a set of pseudoknot-free substructures and approximates a base-pairing probability distribution that considers pseudoknots, leading to the capability of modeling a wide class of pseudoknots and running quite fast. In addition, we propose a heuristic algorithm for refining base-paring probabilities to improve the prediction accuracy of IPknot. The problem of maximizing expected accuracy is solved by using integer programming with threshold cut. We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given. IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.

Availability: The program of IPknot is available at http://www.ncrna.org/software/ipknot/. IPknot is also available as a web server at http://rna.naist.jp/ipknot/.

Contact: satoken@k.u-tokyo.ac.jp; ykato@is.naist.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

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A schematic diagram of the iterative refinement algorithm for the base-pairing probability matrix. A constraint on secondary structure for each level is denoted by a variant of the dot-parenthesis format: a matching parenthesis ‘()’ denotes an allowed base pair, a character ‘x’ indicates an unpaired base, and a dot ‘.’ is used for an unconstrained base.
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Figure 4: A schematic diagram of the iterative refinement algorithm for the base-pairing probability matrix. A constraint on secondary structure for each level is denoted by a variant of the dot-parenthesis format: a matching parenthesis ‘()’ denotes an allowed base pair, a character ‘x’ indicates an unpaired base, and a dot ‘.’ is used for an unconstrained base.

Mentions: We propose an iterative algorithm that refines the base-pairing probabilities used in the objective function of our method. The basic idea is that the base-pairing probabilities are improved by the secondary structures predicted at the first stage, and then a new prediction is performed by the improved base-pairing probabilities (see also Fig. 4).Fig. 4.


IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming.

Sato K, Kato Y, Hamada M, Akutsu T, Asai K - Bioinformatics (2011)

A schematic diagram of the iterative refinement algorithm for the base-pairing probability matrix. A constraint on secondary structure for each level is denoted by a variant of the dot-parenthesis format: a matching parenthesis ‘()’ denotes an allowed base pair, a character ‘x’ indicates an unpaired base, and a dot ‘.’ is used for an unconstrained base.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117384&req=5

Figure 4: A schematic diagram of the iterative refinement algorithm for the base-pairing probability matrix. A constraint on secondary structure for each level is denoted by a variant of the dot-parenthesis format: a matching parenthesis ‘()’ denotes an allowed base pair, a character ‘x’ indicates an unpaired base, and a dot ‘.’ is used for an unconstrained base.
Mentions: We propose an iterative algorithm that refines the base-pairing probabilities used in the objective function of our method. The basic idea is that the base-pairing probabilities are improved by the secondary structures predicted at the first stage, and then a new prediction is performed by the improved base-pairing probabilities (see also Fig. 4).Fig. 4.

Bottom Line: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes.We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given.IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. satoken@k.u-tokyo.ac.jp

ABSTRACT

Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy.

Results: We propose IPknot, a novel computational method for predicting RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure. IPknot decomposes a pseudoknotted structure into a set of pseudoknot-free substructures and approximates a base-pairing probability distribution that considers pseudoknots, leading to the capability of modeling a wide class of pseudoknots and running quite fast. In addition, we propose a heuristic algorithm for refining base-paring probabilities to improve the prediction accuracy of IPknot. The problem of maximizing expected accuracy is solved by using integer programming with threshold cut. We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given. IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.

Availability: The program of IPknot is available at http://www.ncrna.org/software/ipknot/. IPknot is also available as a web server at http://rna.naist.jp/ipknot/.

Contact: satoken@k.u-tokyo.ac.jp; ykato@is.naist.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Show MeSH