ArticleNucleic acids research2015
Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Characterizing RNA 3D structural features from DMS reactivity.Nucleic acids research · 2026Article
- ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.Nucleic acids research · 2026Article
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- Recent Advances in Chemical Probing Strategies for RNA Structure Determination In Vivo.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Exploitation of Biocomputational Approaches for siRNA-Mediated Gene Silencing of HPV: A Novel Therapeutic Strategy for Cervical Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- The Unpaved Road of Non-Coding RNA Structure-Function Relationships: Current Knowledge, Available Methodologies, and Future Trends.Non-coding RNA · 2025Review
- DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures.Computational and structural biotechnology journal · 2024Article
- A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS.RNA (New York, N.Y.) · 2024Article
- DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning.Journal of chemical information and modeling · 2024Article
- Gene silencing of Helicobacter pylori through newly designed siRNA convenes the treatment of gastric cancer.Cancer medicine · 2023Article
- Mutation signature filtering enables high-fidelity RNA structure probing at all four nucleobases with DMS.Nucleic acids research · 2023Article
- RNA structure determination: From 2D to 3D.Fundamental research · 2023Review
- Mutation signature filtering enables high-fidelity RNA structure probing at all four nucleobases with DMS.bioRxiv : the preprint server for biology · 2023Article
- rRNA expansion segment 7 in eukaryotes: from Signature Fold to tentacles.Nucleic acids research · 2022Article
- Advances and opportunities in RNA structure experimental determination and computational modeling.Nature methods · 2022Review
- Acylation probing of "generic" RNA libraries reveals critical influence of loop constraints on reactivity.Cell chemical biology · 2022Article
- Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure.Nature communications · 2022Article
- SSRTool: A web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability.Computational and structural biotechnology journal · 2022Article
- Predicting the Structure of a Viroid : Structure, Structure Distribution, Consensus Structure, and Structure Drawing.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.