ReviewFrontiers in genetics2020
Advances in RNA 3D Structure Modeling Using Experimental Data.
Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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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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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.
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Who cites it
43 citing papers in PubMed, 83 citations in OpenAlex.
- AI meets RNA: revolutionizing structure prediction.Science China. Life sciences · 2025Article
- Comprehensive datasets for RNA design, machine learning, and beyond.Scientific reports · 2025Article
- Transcription regulation by RNA-induced structural strain in duplex DNA.Nucleic acids research · 2025Article
- RNA sample optimization for cryo-EM analysis.Nature protocols · 2025Review
- Principles of ion binding to RNA inferred from the analysis of a 1.55 Å resolution bacterial ribosome structure - Part I: Mg2.Nucleic acids research · 2025Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.Genomics, proteomics & bioinformatics · 2024Article
- Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data.Nucleic acids research · 2024Article
- SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.Nucleic acids research · 2024Article
- State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction.NAR genomics and bioinformatics · 2024Article
- Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot.PLoS computational biology · 2024Article
- RNAvigate: efficient exploration of RNA chemical probing datasets.Nucleic acids research · 2024Article
- Structural and computational studies of HIV-1 RNA.RNA biology · 2024Review
- sRNA Structural Modeling Based on NMR Data.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Article
- A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.Briefings in bioinformatics · 2023Review
- tRNA renovatio: Rebirth through fragmentation.Molecular cell · 2023Review
- RNAMotifComp: a comprehensive method to analyze and identify structurally similar RNA motif families.Bioinformatics (Oxford, England) · 2023Article
- Automatic generation of pseudoknotted RNAs taxonomy.BMC bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
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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.