ArticleRNA biology2013
The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective.
Article in RNA biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Thermodynamic prediction of RNA cellular activity from sequence via conformational ensembles.Cell · 2026Article
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences.Nucleic acids research · 2025Article
- JAX-RNAfold: scalable differentiable folding.Bioinformatics (Oxford, England) · 2025Article
- In vivo-like nearest neighbor parameters improve prediction of fractional RNA base-pairing in cells.Nucleic acids research · 2023Article
- Article
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
- Fitness functions for RNA structure design.Nucleic acids research · 2023Article
- Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.PLoS computational biology · 2023Article
- RNA Secondary Structure Prediction Based on Energy Models.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Advances and opportunities in RNA structure experimental determination and computational modeling.Nature methods · 2022Review
- Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.Bioinformatics (Oxford, England) · 2022Article
- Thoughts on how to think (and talk) about RNA structure.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Secondary structure prediction for RNA sequences including NNature communications · 2022Article
- Caveats to Deep Learning Approaches to RNA Secondary Structure Prediction.Frontiers in bioinformatics · 2022Article
- Evolutionary conservation of RNA sequence and structure.Wiley interdisciplinary reviews. RNA · 2021Review
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- RASP: an atlas of transcriptome-wide RNA secondary structure probing data.Nucleic acids research · 2021Article
- RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.Nature communications · 2019Article
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Abstract
Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.
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