IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming
By Kengo Sato, Yuki Kato, Michiaki Hamada, Tatsuya Akutsu, Kiyoshi Asai. Bioinform., 2011
311 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3df2d09bb3654d4626336d12a53bd6f21bbb719c
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In Molecular Biology
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Citations in the corpus
- RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (2019, methodology): “We downloaded the stand-alone version of mxfold 33 (available at https:// github.com/keio-bioinformatics/mxfold), ContextFold 16 (available at https://www. cs.bgu.ac.il/negevcb/contextfold/), CONTRAfold 14 (available at http://contra. stanford.edu/contrafold/), Knotty 24 (available at https://github”
- Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots (2013, methodology): “2) by performing additional benchmarks with the programs ProbKnot (37),DotKnot+KLandDotKnot−KL(whereKL indicateswhether kissing loops are included) (38, 39), ipknot (40), pknotsRG-mfe (41), and HotKnots (1, 42) (Tables S3 and S4).”
- RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “Furthermore, multiple wrapper tools of RNA secondary structure prediction are implemented (Figure 3f), including RNAsubopt, RNAeval, RNAfold from ViennaRNA (45), CentroidFold (46), Con-textFold (47), MC-Fold (53) and IPknot (54).”