ArticleRNA (New York, N.Y.)2010
Evaluation of the information content of RNA structure mapping data for secondary structure prediction.
Article in RNA (New York, N.Y.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Transforming Medicine: Advances in Gene Therapy, Immunotherapy, and Targeted Cures.Current protein & peptide science · 2025Review
- How Parameters Influence SHAPE-Directed Predictions.Methods in molecular biology (Clifton, N.J.) · 2024Article
- RSCanner: rapid assessment and visualization of RNA structure content.Bioinformatics (Oxford, England) · 2023Article
- Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-native folding intermediates.Molecular cell · 2021Article
- On the Problem of Reconstructing a Mixture of RNA Structures.Bulletin of mathematical biology · 2020Article
- Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications.Chemical reviews · 2020Review
- HiPR: High-throughput probabilistic RNA structure inference.Computational and structural biotechnology journal · 2020Article
- Challenges and approaches to predicting RNA with multiple functional structures.RNA (New York, N.Y.) · 2018Review
- Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.Nature communications · 2018Article
- Modeling RNA secondary structure folding ensembles using SHAPE mapping data.Nucleic acids research · 2018Article
- LncRNA secondary structure in the cardiovascular system.Non-coding RNA research · 2017Review
- Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo.Biophysical journal · 2017Article
- Modeling RNA Secondary Structure with Sequence Comparison and Experimental Mapping Data.Biophysical journal · 2017Article
- Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions.Quantitative biology (Beijing, China) · 2017Article
- Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution.Wiley interdisciplinary reviews. RNA · 2017Review
- Rtools: a web server for various secondary structural analyses on single RNA sequences.Nucleic acids research · 2016Article
- Probing-directed identification of novel structured RNAs.RNA biology · 2016Article
- Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data.Nucleic acids research · 2015Article
- Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark.Nucleic acids research · 2015Article
- Rational experiment design for sequencing-based RNA structure mapping.RNA (New York, N.Y.) · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating "decoy" RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl(2)). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.
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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.