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Updated: Jun 16, 2026

09:04
Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids
Published on: September 21, 2017
LinearCapR: linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span
Takumi Otagaki1, Hiroaki Hosokawa1, Tsukasa Fukunaga2
1The Department of Computational Biology and Medical Sciences (CBMS), The University of Tokyo, Chiba, 277-8561, Japan.
Bioinformatics (Oxford, England)
|June 15, 2026
Summary
LinearCapR offers a new, fast method for RNA structural analysis, accurately predicting RNA folding and function. This tool enables the study of large RNA molecules like viral genomes.
Area of Science:
- Computational Biology
- Bioinformatics
- Molecular Biology
Background:
- RNA secondary structures are crucial for molecular interactions and function.
- Existing computational tools struggle with large RNA molecules due to time complexity or approximations.
- Accurate structural-context probabilities are vital for understanding RNA folding dynamics.
Purpose of the Study:
- To develop a computationally efficient method for RNA structural-context analysis.
- To enable the study of large RNA molecules and their functional implications.
- To overcome the limitations of existing RNA folding prediction tools.
Main Methods:
- Introduced LinearCapR, a novel algorithm based on beam-pruned Stochastic Context Free Grammars.
- Enabled linear-time, span-unrestricted computation of structural-context marginalized probabilities.
- Applied the method to analyze the full SARS-CoV-2 genome.
Main Results:
- LinearCapR achieves linear time complexity and retains global ensemble features.
- Demonstrated superior predictive power on the bpRNA-1m(90) dataset, especially for complex structures.
- Successfully analyzed long RNA sequences, including viral genomes.
Conclusions:
- LinearCapR provides a powerful, efficient framework for RNA structural-context analysis.
- The tool is essential for studying large RNAs, viral genomes, and non-coding RNAs.
- Enables advanced downstream analyses like RNA-binding protein site prediction.
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