NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure
By Douglas H. Turner, D. Mathews. Nucleic Acids Res., 2009
558 citations recorded by the source corpus.
The Nearest Neighbor Database (NNDB, http://rna.urmc.rochester.edu/NNDB) is a web-based resource for disseminating parameter sets for predicting nucleic acid secondary structure stabilities. For each set of parameters, the database includes the set of rules with descriptive text, sequence-dependent parameters in plain text and html, literature references to experiments and usage tutorials. The initial release covers parameters for predicting RNA folding free energy and enthalpy changes.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/d254563a9329337037ac2c038f7679c8d9f161ce
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Kevin M. Weeks
- Ivo L. Hofacker
- Hakim Tafer
- Kengo Sato
- Yasubumi Sakakibara
- Manato Akiyama
- Stanislav Bellaousov
- Christopher W. Leonard
In Molecular Biology
- D. Mathews
- Ivo L. Hofacker
- Peter F. Stadler
- Stephan H. Bernhart
- Hakim Tafer
- Christopher W. Leonard
- Stanislav Bellaousov
- Ronny Lorenz
Citations in the corpus
- 3D RNA and Functional Interactions from Evolutionary Couplings. (2015, unknown): “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.”
- ViennaRNA Package 2.0 (2011, methodology): “Here we provide a parameter set compiled by Douglas Turner and David Math-ews [67] from published data, incorporating in particular earlier work by the group of John SantaLucia [68].”
- Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots (2013, methodology): “Q1 riboswitch (16, 17), human telomerase RNA (18), tRNA (19), TPP riboswitch (20), severe acute respiratory syndrome (SARS) corona virus pseudoknot (21), cyclic-di-GMP riboswitch (22), M-Box riboswitch (23), bI3 group I intron P546 domain (24), SAM I riboswitch (25), Azoarcus group I intron (26), ly”
- RNA secondary structure prediction using deep learning with thermodynamic integration (2020, methodology): “The most popular approach for predicting RNA secondary structures is based on thermodynamic models such as Turner’s nearest neighbor model (2, 3), in which a secondary structure is decomposed into several characteristic substructures, called nearest neighbor loops, such as hairpin loops, internal lo”