Prediction of RNA secondary structure by free energy minimization.

By D. Mathews, Douglas H. Turner. Current Opinion in Structural Biology, 2006

420 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/df7ae046fb1d6e10391fb3bcd2ad41d552022e68

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Jessica S. Reuter
2. Zhiyong Wang
3. Howard M. Salis
4. Xin Gao
5. D. Mathews
6. Le Song
7. James C. Liao

In Molecular Biology

1. Jessica S. Reuter
2. Zhiyong Wang
3. Marta Szachniuk
4. Mariusz Popenda
5. Ryszard W. Adamiak
6. Katarzyna J Purzycka
7. Maciej Antczak
8. Piotr Lukasiak

Citations in the corpus

• RNAstructure: software for RNA secondary structure prediction and analysis (2010, background): “One of the first steps to understanding the mechanism of action of an RNA is to determine its structure [9].”
• The ribosome binding site calculator. (2011, background): “…dynamic programming to implicitly enumerate all possible RNA secondary structures, without explicit calculation of every structure’s free energy, in order to find ones with minimal or near-minimal energies (Dirks et al., 2007; Gruber et al., 2008; Markham and Zuker, 2008; Mathews and Turner, 2006).”
• RNA Secondary Structure Prediction By Learning Unrolled Algorithms (2020, background): “Pseudoknots make up roughly 1.4% of base-pairs (Mathews & Turner, 2006), and are overrepresented in functionally important regions (Hajdin et al., 2013; Staple & Butcher, 2005).”
• UFold: fast and accurate RNA secondary structure prediction with deep learning (2020, neutral): “If the secondary structure contains only nested base pairing, the energy minimization can be efficiently solved through dynamic programming, such as those implemented in Vienna RNAfold (18), MFold (19), RNAstructure (20) and CONTRAfold (21).”
• 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).”
• Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, background): “The base pairing states can be predicted using some secondary structure prediction programs (Akutsu, 2000; Do et al., 2006; Eddy and Durbin, 1994; Gardner and Giegerich, 2004; Knudsen and Hein, 2003; Mathews and Turner, 2006; Poolsap et al., 2009; Zuker, 2003) with reasonable accuracy.”