Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.

By D. Mathews, M. Disney, Jessica L. Childs, S. Schroeder, M. Zuker, Douglas H. Turner. Proceedings of the National Academy of Sciences of the United States of America, 2004

1,596 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dccef7d2a347f0a860902f2c471ddef63147f4e0

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Citations in the corpus

Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization. (2004, methodology): “For example, the Candida albicans 5S rRNA is well predicted both without and with constraints derived from in vivo chemical modification (Mathews et al. 2004), but the constraints still serve to improve the fidelity of prediction.”
Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution (2006, background): “For RNAs that do not contain pseudoknots, we find the RNAstructure progra”
Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction (2004, methodology): “RNAstructure uses a recently revised set of parameters [53], and PKNOTS uses an earlier set of thermodynamic parameters [54].”
Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots (2013, methodology): “The accuracy of secondary structure prediction is improved dramatically by including experimental information as restraints (14, 15).”
RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. (2005, methodology): “Efficient algorithms for computing the minimum free energy (MFE) structure and a set of suboptimal structures (Zuker and Stiegler 1981; Mathews et al. 1999, 2004) are based on free energy parameters that are estimated or extrapolated from chemical melting experiments (Xia et al. 1998; Mathews et…”
RNAstructure: web servers for RNA secondary structure prediction and analysis (2013, background): “Fold (33,34) Predict the lowest free energy structure and a set of low free energy structures for a sequence.”
4SALE – A tool for synchronous RNA sequence and secondary structure alignment and editing (2006, methodology): “The structure output converted to Vienna style DotBracket can be created from any desired RNA folding program, e.g., RNAfold, Mfold [33] or RNAStructure [ 34 ].”
RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP) (2014, neutral): “…to read out the results of enzymatic, SHAPE or dimethyl sulfate (DMS) probing have provided comprehensive support for large-scale comparative trends in transcript structure but have not been shown to yield accurate models of complex secondary structures or enable discovery of new motifs 4–11 .”
ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules (2016, methodology): “Structures are predicted using the RNAfold program of the Vienna package (47,48), and the structure with the lowest free energy is selected.”
Automated 3D structure composition for large RNAs (2012, methodology): “Based on different algorithms, in silico RNA secondary structure prediction methods (5,6) have recently been strengthened by incorporating constraints from chemical probing (7), primarily SHAPE (8).”
3D RNA and Functional Interactions from Evolutionary Couplings. (2015, positive): “Energy calculations were performed using RNAeval (Lorenz et al., 2011; Mathews et al., 2004; Turner and Mathews, 2010) with the default settings from the online server http://rna.tbi.univie.ac.at/cgi-bin/RNAeval.cgi.”