ArticlePloS one2012
Integrating chemical footprinting data into RNA secondary structure prediction.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Fine-scale structural information substantially improves mRNA therapeutic stability prediction.Molecular therapy. Nucleic acids · 2026Article
- Distribution and structural diversity of type IV internal ribosome entry sites.RNA (New York, N.Y.) · 2026Article
- Recent Advances in Chemical Probing Strategies for RNA Structure Determination In Vivo.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions.Proteins · 2026Article
- A method for estimating energy parameters of RNAs by differentiating base-pairing probabilities.NAR genomics and bioinformatics · 2025Article
- Sequence, structure, and affinity of miR-34a binding sites determine repression efficacy.Nucleic acids research · 2025Article
- Consistent features observed in structural probing data of eukaryotic RNAs.NAR genomics and bioinformatics · 2025Article
- Prediction of Circular RNA Secondary Structures and Their Targets.Advances in experimental medicine and biology · 2025Review
- Decryption of sequence, structure, and functional features of SINE repeat elements in SINEUP non-coding RNA-mediated post-transcriptional gene regulation.Nature communications · 2024Article
- Limits of experimental evidence in RNA secondary structure prediction.Frontiers in bioinformatics · 2024Article
- Led-Seq: ligation-enhanced double-end sequence-based structure analysis of RNA.Nucleic acids research · 2023Article
- RNA:RNA interaction in ternary complexes resolved by chemical probing.RNA (New York, N.Y.) · 2023Article
- RNA secondary structure packages evaluated and improved by high-throughput experiments.Nature methods · 2022Article
- Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses.Viruses · 2022Review
- SHAPE-guided RNA structure homology search and motif discovery.Nature communications · 2022Article
- Stability Modelling of mRNA Vaccine Quality Based on Temperature Monitoring throughout the Distribution Chain.Pharmaceutics · 2022Review
- Evolution and structural variations in chloroplast tRNAs in gymnosperms.BMC genomics · 2021Article
- Theoretical basis for stabilizing messenger RNA through secondary structure design.Nucleic acids research · 2021Article
- Evolutionary conservation of RNA sequence and structure.Wiley interdisciplinary reviews. RNA · 2021Review
1 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.
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