ArticleRNA (New York, N.Y.)2016
Data-directed RNA secondary structure prediction using probabilistic modeling.
Article in RNA (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed, 45 citations in OpenAlex.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- 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
- Enhanced RNA secondary structure prediction through integrative deep learning and structural context analysis.Nucleic acids research · 2025Article
- Consistent features observed in structural probing data of eukaryotic RNAs.NAR genomics and bioinformatics · 2025Article
- DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning.Journal of chemical information and modeling · 2024Article
- RNA secondary structure packages evaluated and improved by high-throughput experiments.Nature methods · 2022Article
- Advances and opportunities in RNA structure experimental determination and computational modeling.Nature methods · 2022Review
- ShapeSorter: a fully probabilistic method for detecting conserved RNA structure features supported by SHAPE evidence.Nucleic acids research · 2022Article
- Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome.Scientific reports · 2022Article
- SSRTool: A web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability.Computational and structural biotechnology journal · 2022Article
- Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites.RNA biology · 2021Article
- Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets.NAR genomics and bioinformatics · 2021Article
- Human IRES Atlas: an integrative platform for studying IRES-driven translational regulation in humans.Database : the journal of biological databases and curation · 2021Article
- Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias.The journal of physical chemistry. B · 2021Article
- SHAPER: A Web Server for Fast and Accurate SHAPE Reactivity Prediction.Frontiers in molecular biosciences · 2021Article
- IPANEMAP: integrative probing analysis of nucleic acids empowered by multiple accessibility profiles.Nucleic acids research · 2020Article
- An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots.BMC genomics · 2019Article
- dStruct: identifying differentially reactive regions from RNA structurome profiling data.Genome biology · 2019Article
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.
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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 Socratic graph.