ArticleNucleic acids research2018
bpRNA: large-scale automated annotation and analysis of RNA secondary structure.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.
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Who cites it
131 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency.Molecular cell · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- Structure-aware deep learning enhances m6A prediction and reveals cell type-associated RNA structural signatures.PLoS computational biology · 2026Article
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- PlanarFold: a coarse-grained molecular dynamics model of RNA in two-dimensional space.Nature communications · 2026Article
- LinearCapR: linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits.Bioinformatics (Oxford, England) · 2026Article
- Methods for Analyzing RNA Pseudoknots via Chord Diagrams and Intersection Graphs.Bulletin of mathematical biology · 2026Article
- Graph-based RNA structural representation reveals determinants of subcellular localization.Briefings in bioinformatics · 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
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Retentive Network promotes efficient RNA language modeling of long sequences.Communications biology · 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
- Decoding RNA triple helices: identification from sequence and secondary structure.Briefings in bioinformatics · 2026Article
- Sequence optimization of a DNA aptamer inhibiting COVID-19 infection guided by analysis of secondary structure distribution.Computational and structural biotechnology journal · 2026Article
- Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.Proteins · 2026Article
71 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.
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