RNA secondary structure modeling at consistent high accuracy using differential SHAPE
By Greggory M. Rice, Christopher W. Leonard, Kevin M. Weeks. 2014
99 citations recorded by the source corpus.
Determining the secondary structure of RNA is a challenging and incompletely solved problem. Using a three-reagent SHAPE experiment to detect local nucleotide flexibility and noncanonical interactions, it is possible to model structurally complex RNAs, including those previously shown to be especially difficult to model, with accuracies consistently exceeding recovery of 90% of canonical base pairs.
Subfields: Molecular Biology
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In Molecular Biology
- Rhiju Das
- Janusz M. Bujnicki
- N. V. Dokholyan
- Eric Westhof
- Wipapat Kladwang
- Fang-Chieh Chou
- Marta Szachniuk
- C. Cheng
Citations in the corpus
- Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, neutral): “For instance, it achieved an F1 score of 0.60 on structured domains in the dengue virus transcriptome (Supplementary Table 19), aligning with results from mutational profiling (RING-MaP) 42,43 .”
- RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP) (2014, unknown): “These innovations include improved energy models, the ability to model pseudoknots, and concise strategies for detecting tertiary and noncanonical interactions 19,22 .”
- RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, background): “As has been discussed previously (Cordero et al. 2012a; Leonard et al. 2013; Rice et al. 2014), protection of nucleotides may signal non-Watson Crick rather than Watson Crick pairing in the structure, but current methods generally distinguish these possibilities (Figs.”