CentroidFold: a web server for RNA secondary structure prediction
By Kengo Sato, Michiaki Hamada, Kiyoshi Asai, Toutai Mituyama. Nucleic Acids Res., 2009
315 citations recorded by the source corpus.
The CentroidFold web server (http://www.ncrna.org/centroidfold/) is a web application for RNA secondary structure prediction powered by one of the most accurate prediction engine. The server accepts two kinds of sequence data: a single RNA sequence and a multiple alignment of RNA sequences. It responses with a prediction result shown as a popular base-pair notation and a graph representation. PDF version of the graph representation is also available. For a multiple alignment sequence, the server predicts a common secondary structure. Usage of the server is quite simple. You can paste a single RNA sequence (FASTA or plain sequence text) or a multiple alignment (CLUSTAL-W format) into the textarea then click on the ‘execute CentroidFold’ button. The server quickly responses with a prediction result. The major advantage of this server is that it employs our original CentroidFold software as its prediction engine which scores the best accuracy in our benchmark results. Our web server is freely available with no login requirement.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/dde2049e2aa47e63178af10e5497e170a4a4289a
How researchers see this paper
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In Molecular Biology
- Jack Hanson
- Kuldip K. Paliwal
- Yaoqi Zhou
- Jaswinder Singh
- Kengo Sato
- Yasubumi Sakakibara
- Manato Akiyama
- Yuedong Yang
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”
- Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, methodology): “These methods usually do not consider all base pairs obtained from tertiary interactions [14, 26, 27], which may miss essential information in their predictions.”
- RNA secondary structure prediction using deep learning with thermodynamic integration (2020, background): “16 (39, 40), RNAfold in Vienna RNA package 2.”
- UFold: fast and accurate RNA secondary structure prediction with deep learning (2020, unknown): “Efficient dynamic programming algorithms that sample suboptimal secondary structures from the Boltzmann ensembles of structures have also been proposed, for example, Centroid-Fold (26).”