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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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An illustration of the constraints of the IP formulation. The diagrams (a) and (b) correspond to the constraints (5) and (6), respectively. Note that at most one variable shown by a broken curved line can take a value 1. The diagram (c) corresponds to the constraint (7).
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Figure 3: An illustration of the constraints of the IP formulation. The diagrams (a) and (b) correspond to the constraints (5) and (6), respectively. Note that at most one variable shown by a broken curved line can take a value 1. The diagram (c) corresponds to the constraint (7).

Mentions: Maximization of the approximate expected gain (3) can be solved by the IP problem as follows:(4)(5)(6)(7)Since Equation (4) is an instantiation of the approximate estimator (3) and the threshold cut technique is applicable to Equation (3), we need to consider only base pairs y(p)ij whose base-pairing probabilities pij are larger than θ(p)=1/(γ(p)+1). The constraint (5) means that each base xi can be paired with at most one base. (Fig. 3a). The constraint (6) disallows pseudoknots within the same level p (Fig. 3b). The constraint (7) ensures that each base pair at the level p is pseudoknotted to at least one base pair at every lower level q<p (Fig. 3c).Fig. 3.


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)

An illustration of the constraints of the IP formulation. The diagrams (a) and (b) correspond to the constraints (5) and (6), respectively. Note that at most one variable shown by a broken curved line can take a value 1. The diagram (c) corresponds to the constraint (7).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: An illustration of the constraints of the IP formulation. The diagrams (a) and (b) correspond to the constraints (5) and (6), respectively. Note that at most one variable shown by a broken curved line can take a value 1. The diagram (c) corresponds to the constraint (7).
Mentions: Maximization of the approximate expected gain (3) can be solved by the IP problem as follows:(4)(5)(6)(7)Since Equation (4) is an instantiation of the approximate estimator (3) and the threshold cut technique is applicable to Equation (3), we need to consider only base pairs y(p)ij whose base-pairing probabilities pij are larger than θ(p)=1/(γ(p)+1). The constraint (5) means that each base xi can be paired with at most one base. (Fig. 3a). The constraint (6) disallows pseudoknots within the same level p (Fig. 3b). The constraint (7) ensures that each base pair at the level p is pseudoknotted to at least one base pair at every lower level q<p (Fig. 3c).Fig. 3.

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