ArticleNucleic acids research2013
Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- When does additional information improve accuracy of RNA secondary structure prediction?bioRxiv : the preprint server for biology · 2026Article
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Consistent features observed in structural probing data of eukaryotic RNAs.NAR genomics and bioinformatics · 2025Article
- RNA structure profiling at single-cell resolution reveals new determinants of cell identity.Nature methods · 2024Article
- A Guide to Computational Cotranscriptional Folding Featuring the SRP RNA.Methods in molecular biology (Clifton, N.J.) · 2024Article
- How Parameters Influence SHAPE-Directed Predictions.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.Bioinformatics (Oxford, England) · 2022Article
- Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure.Nature communications · 2022Article
- Next-generation sequencing: A new avenue to understand viral RNA-protein interactions.The Journal of biological chemistry · 2022Review
- Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites.RNA biology · 2021Article
- Evolutionary conservation of RNA sequence and structure.Wiley interdisciplinary reviews. RNA · 2021Review
- Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets.NAR genomics and bioinformatics · 2021Article
- Characteristic chemical probing patterns of loop motifs improve prediction accuracy of RNA secondary structures.Nucleic acids research · 2021Article
- Using SHAPE-MaP To Model RNA Secondary Structure and Identify 3'UTR Variation in Chikungunya Virus.Journal of virology · 2020Article
- On the Problem of Reconstructing a Mixture of RNA Structures.Bulletin of mathematical biology · 2020Article
- RNA structure prediction using positive and negative evolutionary information.PLoS computational biology · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence.
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