Evidence map›Paper›PMID 31754698›Full record

ReviewNucleic acids research2020

A guide to computational methods for G-quadruplex prediction.

Emilia Puig Lombardi, Arturo Londoño-Vallejo

Erratum issuedAbstract readReview
In one paragraph

Review in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Coding relationship links RNA G-quadruplexes and protein RGG motifs in RNA-binding protein autoregulation.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  19. Article
  20. Frontiers in cellular and infection microbiology · 2025
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Emilia Puig LombardiTelomeres and Cancer Laboratory, Institut Curie, PSL Research University, Sorbonne Universités, CNRS UMR3244, 75005 Paris, France.
Arturo Londoño-VallejoTelomeres and Cancer Laboratory, Institut Curie, PSL Research University, Sorbonne Universités, CNRS UMR3244, 75005 Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Guanine-rich nucleic acids can fold into the non-B DNA or RNA structures called G-quadruplexes (G4). Recent methodological developments have allowed the characterization of specific G-quadruplex structures in vitro as well as in vivo, and at a much higher throughput, in silico, which has greatly expanded our understanding of G4-associated functions. Typically, the consensus motif G3+N1-7G3+N1-7G3+N1-7G3+ has been used to identify potential G-quadruplexes from primary sequence. Since, various algorithms have been developed to predict the potential formation of quadruplexes directly from DNA or RNA sequences and the number of studies reporting genome-wide G4 exploration across species has rapidly increased. More recently, new methodologies have also appeared, proposing other estimates which consider non-canonical sequences and/or structure propensity and stability. The present review aims at providing an updated overview of the current open-source G-quadruplex prediction algorithms and straightforward examples of their implementation.

Indexed as

GenomeG-QuadruplexesModels, StatisticalSoftwareAnimalsBase SequenceBenchmarkingDrosophila melanogasterGuanineHumansMachine LearningMiceModels, MolecularZebrafishGuanine

Identifiers

PMID31754698
PMCPMC6943126

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.