ArticleNucleic acids research2018
Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 53 citations in OpenAlex.
- All-at-once RNA folding with 3D motif prediction framed by evolutionary information.Nature methods · 2025Article
- ARTEM: a method for RNA and DNA tertiary motif identification with backbone permutations.Genome biology · 2025Article
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- PERFUMES: pipeline to extract RNA functional motifs and exposed structures.Bioinformatics (Oxford, England) · 2024Article
- Concurrent prediction of RNA secondary structures with pseudoknots and local 3D motifs in an integer programming framework.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures.Nucleic acids research · 2023Article
- RNAMotifComp: a comprehensive method to analyze and identify structurally similar RNA motif families.Bioinformatics (Oxford, England) · 2023Article
- RNAsmc: A integrated tool for comparing RNA secondary structure and evaluating allosteric effects.Computational and structural biotechnology journal · 2023Article
- Tree diet: reducing the treewidth to unlock FPT algorithms in RNA bioinformatics.Algorithms for molecular biology : AMB · 2022Article
- On the predictibility of A-minor motifs from their local contexts.RNA biology · 2022Article
- RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures.Bioinformatics (Oxford, England) · 2021Article
- Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs.PLoS computational biology · 2021Article
- In vitro selected GUAA tetraloop-binding receptors with structural plasticity and evolvability towards natural RNA structural modules.Nucleic acids research · 2021Article
- Combining fragment docking with graph theory to improve ligand docking for homology model structures.Journal of computer-aided molecular design · 2020Article
- Augmented base pairing networks encode RNA-small molecule binding preferences.Nucleic acids research · 2020Article
- ElTetrado: a tool for identification and classification of tetrads and quadruplexes.BMC bioinformatics · 2020Article
- Automated, customizable and efficient identification of 3D base pair modules with BayesPairing.Nucleic acids research · 2019Article
- CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts.BMC bioinformatics · 2019Article
- Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs.Genes · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.
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.