ArticleRNA (New York, N.Y.)2016
Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures.
Article in RNA (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Nearest Neighbor Parameters for Estimating RNA Folding Stability withbioRxiv : the preprint server for biology · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- Making Ends Meet or Just Meeting at the Ends? Assessing End-to-End Distance in Folded RNA Sequences and Other Branched Structures.Bulletin of mathematical biology · 2026Article
- Tree_RNA-Align: RNA Secondary Structure Clustering and Classification Based on Tree-Structure Alignment.International journal of molecular sciences · 2026Article
- Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.Bioinformatics (Oxford, England) · 2026Article
- Nearest Neighbor Parameters for Estimating the Folding Stability of RNA Including Pseudouridine.bioRxiv : the preprint server for biology · 2026Article
- An efficient algorithm for exploring RNA branching conformations under the nearest-neighbor thermodynamic model.Algorithms for molecular biology : AMB · 2026Article
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Retentive Network promotes efficient RNA language modeling of long sequences.Communications biology · 2026Article
- Exploring the Boundaries of Modern Quantum Annealers with RNA Structure Prediction.The journal of physical chemistry. B · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Diverse database and machine learning model to narrow the generalization gap in RNA structure prediction.Science advances · 2026Article
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- StraD: a coarse-grained simulation of co-transcriptional RNA folding.NAR genomics and bioinformatics · 2025Article
- ERNIE-RNA: an RNA language model with structure-enhanced representations.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
12 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.
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