ReviewNature reviews. Genetics2023
High-throughput biochemistry in RNA sequence space: predicting structure and function.
Review in Nature reviews. Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 23 citations in OpenAlex.
- Review
- Decoding RNA-protein interactions using high-throughput methods.RNA biology · 2026Review
- Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers.bioRxiv : the preprint server for biology · 2026Article
- PAIRNet: Predicting PIWI cleavage specificity via position-aware RNA interaction modeling.PLoS computational biology · 2026Article
- Multiplexed single-molecule characterization at the library scale.Nature protocols · 2026Review
- Comprehensive analysis of the tumor immune microenvironment in gastric cancer and peritoneal metastasis based on single-cell RNA sequencing analysis.Scientific reports · 2025Article
- Photodynamic gel-bombs enhance tumor penetration and downstream synergistic therapies.Signal transduction and targeted therapy · 2025Article
- Systematic analysis of cotranscriptional RNA folding using transcription elongation complex display.Nature communications · 2025Article
- Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Exploring the conformational dynamics of the SARS-CoV-2 SL4 hairpin by combining optical tweezers and base analogues.Nanoscale · 2024Article
- tRNA renovatio: Rebirth through fragmentation.Molecular cell · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.
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.