ArticleRNA (New York, N.Y.)2009
New metrics for comparing and assessing discrepancies between RNA 3D structures and models.
Article in RNA (New York, N.Y.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 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
142 citing papers in PubMed.
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- The MODNAP benchmark set for modeling ribose-based modified nucleic acid and nucleic acid-protein complexes: a case study with AlphaFold 3.Nucleic acids research · 2026Article
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- NuConf: a rotamer library for DNA and RNA and its implementation in the protein design software MUMBO.Scientific reports · 2026Article
- Structural analysis of uridine modifications in solved RNA structures.NAR genomics and bioinformatics · 2026Article
- A retroelement-derived mammalian ARC protein exhibits selective RNA recognition and nucleic acid chaperone functions.Nucleic acids research · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Aptamer-linked immobilized sorbent assay for detection of VP1 of foot and mouth disease virus serotype O.Scientific reports · 2026Article
- Article
- DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure prediction.PLoS biology · 2026Article
- Updates to the CASP Infrastructure in 2024.Proteins · 2026Article
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- The RNA-Puzzles Assessments of RNA-Only Targets in CASP16.Proteins · 2026Article
- Assessment of Nucleic Acid Structure Prediction in CASP16.Proteins · 2026Article
- Deep Learning in Modeling Tools for Structural Insights into Protein-RNA Complexes, Bridging Computational and Spectroscopic Approaches.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Predicting Single-Stranded DNA Oligonucleotides 3D Structures: An Open Issue.Computational and structural biotechnology journal · 2026Article
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- RNAdvisor 2: A unified platform for RNA 3D model quality assessment using metrics, scoring functions, and meta-metrics.Scientific reports · 2025Article
82 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To benchmark progress made in RNA three-dimensional modeling and assess newly developed techniques, reliable and meaningful comparison metrics and associated tools are necessary. Generally, the average root-mean-square deviations (RMSDs) are quoted. However, RMSD can be misleading since errors are spread over the whole molecule and do not account for the specificity of RNA base interactions. Here, we introduce two new metrics that are particularly suitable to RNAs: the deformation index and deformation profile. The deformation index is calibrated by the interaction network fidelity, which considers base-base-stacking and base-base-pairing interactions within the target structure. The deformation profile highlights dissimilarities between structures at the nucleotide scale for both intradomain and interdomain interactions. Our results show that there is little correlation between RMSD and interaction network fidelity. The deformation profile is a tool that allows for rapid assessment of the origins of discrepancies.
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.