Evidence map›Paper›PMID 26304547›Full record

ArticleNucleic acids research2015

RAG-3D: a search tool for RNA 3D substructures.

Mai Zahran, Cigdem Sevim Bayrak, Shereef Elmetwaly, Tamar Schlick

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. RNAloops: a database of RNA multiloops.Bioinformatics (Oxford, England) · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. A Fiedler Vector Scoring Approach for Novel RNA Motif Selection.The journal of physical chemistry. B · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mai ZahranBiological Sciences Department, New York City College of Technology, City University of New York, Brooklyn, NY 11201, USA.
Cigdem Sevim BayrakDepartment of Chemistry, New York University, New York, NY 10003, USA.
Shereef ElmetwalyDepartment of Chemistry, New York University, New York, NY 10003, USA.
Tamar SchlickDepartment of Chemistry, New York University, New York, NY 10003, USA Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA schlick@nyu.edu.

Funding

Modeling RNA Tertiary Structure Folding by a Hierarchical FrameworkR01GM100469 · NIGMS · NEW YORK UNIVERSITY · PI SCHLICK, TAMAR · 2011 to 2014
$1.5M
Computational studies of in vitro selection of RNAsR01GM081410 · NIGMS · NEW YORK UNIVERSITY · PI SCHLICK, TAMAR · 2009 to 2012
$1.2M
NIGMS NIH HHS GM081410NIGMS NIH HHS GM100469NIGMS NIH HHS R01 GM081410NIGMS NIH HHS R01 GM100469
6 · The paper itself

Abstract

To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D-a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool-designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.

Indexed as

Models, MolecularSoftwareAlgorithmsDatabases, Nucleic AcidInternetNucleic Acid ConformationRNARNA, Ribosomal, 23SRNA, Small CytoplasmicSignal Recognition Particle7SL RNARNARNA, Ribosomal, 23SRNA, Small CytoplasmicSignal Recognition Particle

Identifiers

PMID26304547
PMCPMC4627073

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.