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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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The PPV–Sensitivity plots of the experiment on the pk168 dataset.
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Figure 6: The PPV–Sensitivity plots of the experiment on the pk168 dataset.

Mentions: Since the pk168 dataset contains shorter sequences than the RS-pk388 dataset, more accurate but computationally expensive prediction algorithms can be applied to this dataset. For the pk168 dataset, we evaluated IPknot with the D&P model (Dirks and Pierce, 2003; 2004) that calculates the exact base-pairing probabilities including a limited class of pseudoknots. Furthermore, our method was compared with the DP-based algorithms including NUPACK (Dirks and Pierce, 2003; 2004) and PKNOTS (Rivas and Eddy, 1999), as well as the competitive methods used in the previous experiment. Figure 6 shows the accuracy on the pk168 dataset, indicating that IPknot with the D&P model is comparable to pknotsRG, which achieves the best accuracy.Fig. 6.


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)

The PPV–Sensitivity plots of the experiment on the pk168 dataset.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 6: The PPV–Sensitivity plots of the experiment on the pk168 dataset.
Mentions: Since the pk168 dataset contains shorter sequences than the RS-pk388 dataset, more accurate but computationally expensive prediction algorithms can be applied to this dataset. For the pk168 dataset, we evaluated IPknot with the D&P model (Dirks and Pierce, 2003; 2004) that calculates the exact base-pairing probabilities including a limited class of pseudoknots. Furthermore, our method was compared with the DP-based algorithms including NUPACK (Dirks and Pierce, 2003; 2004) and PKNOTS (Rivas and Eddy, 1999), as well as the competitive methods used in the previous experiment. Figure 6 shows the accuracy on the pk168 dataset, indicating that IPknot with the D&P model is comparable to pknotsRG, which achieves the best accuracy.Fig. 6.

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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