A comprehensive comparison of comparative RNA structure prediction approaches
By Paul P. Gardner, Robert Giegerich. BMC Bioinformatics, 2004
412 citations recorded by the source corpus.
BackgroundAn increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms.ResultsHere we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance.ConclusionsWe conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/115f96471c9721475bc72ee1bf7bed7efa3630a3
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In Molecular Biology
- Kazutaka Katoh
- Hiroyuki Toh
- Zhiyong Wang
- Ivo L. Hofacker
- Byung-Jun Yoon
- Peter F. Stadler
- Paul P. Gardner
- Chuong B. Do
Citations in the corpus
- Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework (2008, background): “ing to Gardner and Giegerich [15], there are three possible plans to infer common secondary structures from a set of unaligned RNA sequences, align-then-fold (plan A), simultaneous (plan B) and fold-then-align (plan C).”
- A benchmark of multiple sequence alignment programs upon structural RNAs (2005, background): “A second structural RNA alignment approach employs (predicted) structures and aligns these directly [19–21], again these are generally limited in terms of sequence size, but primarily suffer from the inaccuracy of single sequence structure prediction [22–24] (although, pruning low probability base-p”
- UFold: fast and accurate RNA secondary structure prediction with deep learning (2020, neutral): “The second category of methods such as (28–30) analyze multiple sequences to determine points of base covariance within the sequences to help infer base pair positions, and try to predict conserved structures.”
- A fast structural multiple alignment method for long RNA sequences (2008, background): “Compatible base pairs are not true positives but have to be neither inconsistent (one or both nucleotides being a part of a different base pair in the reference structure) nor pseudo-knotted with respect to the reference structure [37].”
- ViennaRNA Package 2.0 (2011, methodology): “For such a benchmark we refer to the literature, e.g. [18,89,90].”
- CONTRAfold: RNA secondary structure prediction without physics-based models (2006, background): “For this reason, computational prediction provides an attractive alternative to empirical discovery of RNA secondary structure [4].”
- 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.”