ArticleRNA (New York, N.Y.)2009
Improved RNA secondary structure prediction by maximizing expected pair accuracy.
Article in RNA (New York, N.Y.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers.
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Who cites it
136 citing papers in PubMed.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
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- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine.bioRxiv : the preprint server for biology · 2026Article
- Recent Advances in Chemical Probing Strategies for RNA Structure Determination In Vivo.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Molecular dynamics simulations based siRNA design against GPR10 reveals stable RNAi therapeutics for hormone-dependent uterine fibroids.Scientific reports · 2025Article
- AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction.bioRxiv : the preprint server for biology · 2025Article
- Expanding the Riboglow-FLIM Toolbox with Different Fluorescence Lifetime-Producing RNA Tags.Biochemistry · 2025Article
- Article
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- eIF4F controls ERK MAPK signaling in melanomas withProceedings of the National Academy of Sciences of the United States of America · 2024Article
- DecoyFinder: Identification of Contaminants in Sets of Homologous RNA Sequences.bioRxiv : the preprint server for biology · 2024Article
- LinearAlifold: Linear-time consensus structure prediction for RNA alignments.Journal of molecular biology · 2024Article
- A systematic search for RNA structural switches across the human transcriptome.Nature methods · 2024Article
76 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Free energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the free energy change nearest-neighbor parameters is used to predict base-pair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum free energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring double-strandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with free energy minimization.
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