ReviewNature biotechnology2026
Integrated experimental and AI innovations for RNA structure determination.
Review in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- Decoding the Structural Complexity of Viral RNAs with SHAPE to Guide Antiviral Therapeutics.Viruses · 2026Review
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
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
Abstract
RNAs act as crucial 'social' mediators within the cell, orchestrating a wide array of biological processes. Their functionality hinges on their complex three-dimensional structures, which dictate stability, binding specificity and molecular interactions. In recent years, a surge of research has focused on solving and/or predicting RNA structures to unlock their functional secrets. However, the dynamic nature and unique physicochemical properties of RNAs pose notable challenges to accurate structural determination. This Perspective reviews recent breakthroughs in RNA structure determination, driven by innovative experimental techniques, such as cryo-electron microscopy, alongside artificial intelligence-based tools inspired by advances in protein structure prediction. We explore how integrative approaches that combine experimental and computational methods are proving particularly powerful in illuminating the RNA world, offering enhanced resolution and scalability. We discuss remaining challenges and opportunities to overcome these hurdles. By integrating experiments with computation, the field is poised to deepen our understanding of RNA biology, paving the way for transformative applications in biotechnology and medicine.
Indexed as
Identifiers
41491252What 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.