ArticleBriefings in bioinformatics2022
Prediction of RNA secondary structure including pseudoknots for long sequences.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- RNA Structure and Its Function.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation.Nature communications · 2025Article
- Genome-wide identification, characterization, and functional analysis of the CHX, SOS, and RLK genes in Solanum lycopersicum under salt stress.Scientific reports · 2025Article
- Effects of Molecular Crowding on the Structure, Folding, Stability, and Catalysis of RNA.Sub-cellular biochemistry · 2025Review
- RNA secondary structure prediction by conducting multi-class classifications.Computational and structural biotechnology journal · 2025Article
- GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings.Computational and structural biotechnology journal · 2025Article
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- Article
- Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.Genomics, proteomics & bioinformatics · 2024Article
- RNA tertiary structure and conformational dynamics revealed by BASH MaP.bioRxiv : the preprint server for biology · 2024Article
- Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.Genes · 2024Article
- Genome-wide identification and evolutionary analysis of the AP2/EREBP, COX and LTP genes in Zea mays L. under drought stress.Scientific reports · 2024Article
- Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials.Communications biology · 2024Article
- Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework.Bioinformatics (Oxford, England) · 2024Article
- Possible involvement of three-stemmed pseudoknots in regulating translational initiation in human mRNAs.PloS one · 2024Article
- NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation.bioRxiv : the preprint server for biology · 2023Article
- 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
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.
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