ExpertRNA: A New Framework for RNA Secondary Structure Prediction
Published Online:19 Apr 2022https://doi.org/10.1287/ijoc.2022.1188
References
- (2013) Ensemble-based prediction of RNA secondary structures. BMC Bioinformatics 14(1):139.Crossref, Google Scholar
- (2008) RNA STRAND: The RNA secondary structure and statistical analysis database. BMC Bioinformatics 9(1):340.Crossref, Google Scholar
- (2007) Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics 23(13):i19–i28.Crossref, Google Scholar
- (2010) Computational approaches for RNA energy parameter estimation. RNA 16(12):2304–2318.Crossref, Google Scholar
- (2018) New algorithms to represent complex pseudoknotted RNA structures in dot-bracket notation. Bioinformatics 34(8):1304–1312.Crossref, Google Scholar
- (2019) Reinforcement Learning and Optimal Control (Athena Scientific, Belmont, MA).Google Scholar
- (2020) Rollout, Policy Iteration, and Distributed Reinforcement Learning (Athena Scientific, Belmont, MA).Google Scholar
- (1996) Neuro-Dynamic Programming (Athena Scientific, Belmont, MA).Google Scholar
- (1997) Rollout algorithms for combinatorial optimization. J. Heuristics 3(3):245–262.Crossref, Google Scholar
- (2013) Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41(D1):D226–D232.Crossref, Google Scholar
- (2020) Machine learning a model for RNA structure prediction. NAR Genomics Bioinformatics 2(4):lqaa090.Google Scholar
- (2012) Structure and function of noncanonical nucleobases. Angewandte Chemie Internat. Edition 51(29):7110–7131.Crossref, Google Scholar
- (2019) Learning to fold RNAs in linear time. Preprint, submitted November 24, https://www.biorxiv.org/content/10.1101/852871v1.Google Scholar
- (2012) RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18(4):610–625.Crossref, Google Scholar
- (1996) Solution structure of an ATP-binding RNA aptamer reveals a novel fold. RNA 2(7):628–640.Google Scholar
- (2006) Contrafold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14):e90–e98.Crossref, Google Scholar
- (2017) Molecular Biology of RNA (Oxford University Press, New York).Google Scholar
- (2014) A single-stranded architecture for cotranscriptional folding of RNA nanostructures. Sci. 345(6198):799–804.Crossref, Google Scholar
- (2014) Toehold switches: De-novo-designed regulators of gene expression. Cell 159(4):925–939.Crossref, Google Scholar
- (2010) The emerging field of RNA nanotechnology. Nature Nanotechnology 5(12):833–842.Crossref, Google Scholar
- (2017) Single-stranded DNA and RNA origami. Sci. 358(6369):eaao2648.Crossref, Google Scholar
- (2020) Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning. Bioinformatics 36(4):1107–1113.Crossref, Google Scholar
- (2013) Conditional dicer substrate formation via shape and sequence transduction with small conditional RNAs. J. Amer. Chemical Soc. 135(46):17322–17330.Crossref, Google Scholar
- (2003) Vienna RNA secondary structure server. Nucleic Acids Res. 31(13):3429–3431.Crossref, Google Scholar
- (2010) CD-HIT suite: A web server for clustering and comparing biological sequences. Bioinformatics 26(5):680–682.Crossref, Google Scholar
- (2000) Modeling RNA folding paths with pseudoknots: Application to hepatitis delta virus ribozyme. Proc. Natl. Acad. Sci. USA 97(12):6515–6520.Crossref, Google Scholar
- (2015) Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams. Bioinformatics 31(20):3377–3379.Crossref, Google Scholar
- (2009) Positive unlabeled learning for data stream classification. Proc. 2009 SIAM Internat. Conf. Data Mining (SIAM), 259–270.Google Scholar
- (2011) Viennarna package 2.0. Algorithms Molecular Biol. 6(1):26.Crossref, Google Scholar
- (2010) Shape-directed RNA secondary structure prediction. Methods 52(2):150–158.Crossref, Google Scholar
- (2011) Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (shape-seq). Proc. Natl. Acad. Sci. USA 108(27):11063–11068.Crossref, Google Scholar
- (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Molecular Biol. 288(5):911–940.Crossref, Google Scholar
- (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA)–Protein Structure 405(2):442–451.Crossref, Google Scholar
- (2017) RNA-puzzles round III: 3d RNA structure prediction of five riboswitches and one ribozyme. RNA 23(5):655–672.Crossref, Google Scholar
- (2018) Critical assessment of methods of protein structure prediction (CASP)—Round XII. Proteins 86(1):7–15.Crossref, Google Scholar
- (2020) RNA origami nanostructures for potent and safe anticancer immunotherapy. ACS Nano 14(4):4727–4740.Crossref, Google Scholar
- (2010) RNAstructure: Software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11(1):129.Crossref, Google Scholar
- (2004) Protein structure prediction using Rosetta. Methods Enzymology 383:66–93.Crossref, Google Scholar
- (2011) Automated RNA tertiary structure prediction from secondary structure and low-resolution restraints. J. Comput. Chemistry 32(10):2232–2244.Crossref, Google Scholar
- (2019) RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nature Comm. 10(1):1–13.Crossref, Google Scholar
- (2017) Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs. PLOS Comput. Biol. 13(11):e1005827.Crossref, Google Scholar
- (2019) ENTRNA: A framework to predict RNA foldability. BMC Bioinformatics 20(1):373.Crossref, Google Scholar
- (2018) Predicting cotranscriptional folding kinetics for riboswitch. J. Physical Chemistry B 122(30):7484–7496.Crossref, Google Scholar
- (2010) Predicting ion binding properties for RNA tertiary structures. Biophysical. J. 99(5):1565–1576.Crossref, Google Scholar
- (2019) DMFold: A novel method to predict RNA secondary structure with pseudoknots based on deep learning and improved base pair maximization principle. Frontiers Genetics 10:143.Crossref, Google Scholar
- (2017) Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best. Nucleic Acids Res. 45(14):8541–8550.Crossref, Google Scholar
- (2020) FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds. Structure 28(8):963–976.Crossref, Google Scholar
- (2020) RNA secondary structure packages ranked and improved by high-throughput experiments. Preprint, submitted May 31, https://www.biorxiv.org/content/10.1101/2020.05.29.124511v1.Google Scholar
- (2006) RNA tertiary structure. Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation.Google Scholar
- (1998) Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 37(42):14719–14735.Crossref, Google Scholar
- Yu AM, Gasper PM, Cheng L, Lai LB, Kaur S, Gopalan V, Chen AA, Lucks JB (2021) Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-native folding intermediates. Molecular Cell 81(4):870–883.Google Scholar
- (2011) Nupack: Analysis and design of nucleic acid systems. J. Comput. Chemistry 32(1):170–173.Crossref, Google Scholar
- (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9(1):133–148.Crossref, Google Scholar
- (1999) Algorithms and thermodynamics for RNA secondary structure prediction: A practical guide. RNA Biochemistry and Biotechnology (Springer), 11–43.Crossref, Google Scholar

