Evidence map›Paper›PMID 22550012›Full record

ArticleGenome research2012

Triplexator: detecting nucleic acid triple helices in genomic and transcriptomic data.

Fabian A Buske, Denis C Bauer, John S Mattick, Timothy L Bailey

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
128citing papers in PubMed, 1 pooled it
–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

128 citing papers in PubMed, 1 synthesis or guideline pooled it, 246 citations in OpenAlex.

  1. Pooled it
  2. HSeeker: an algorithm for systematic H-DNA sequence identification.bioRxiv : the preprint server for biology · 2026
    Article
  3. RNA•DNA:DNA triplex formation byRNA (New York, N.Y.) · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Relevance of DNA tridimensional shape in RNA:DNA:DNA triple helix formation.Computational and structural biotechnology journal · 2026
    Article
  11. Machine Learning Approaches for Predicting RNA-RNA/DNA Interactions.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  12. LncRNA:DNA Binding Prediction Using LongTarget and Its Revised Version.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review

68 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 1 country.

Fabian A BuskeInstitute for Molecular Bioscience, The University of Queensland, Brisbane, 4072 QLD, Australia.
Denis C Bauer
John S Mattick
Timothy L Bailey
The University of Queensland · AUCommonwealth Scientific and Industrial Research Organisation · AUGarvan Institute of Medical Research · AU

Funding

The MEME suite of motif-based sequence analysis toolsR01RR021692 · NCRR · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2005 to 2011
$2.1M
NCRR NIH HHS R01 RR021692NCRR NIH HHS R01 RR021692-01
6 · The paper itself

Abstract

Double-stranded DNA is able to form triple-helical structures by accommodating a third nucleotide strand in its major groove. This sequence-specific process offers a potent mechanism for targeting genomic loci of interest that is of great value for biotechnological and gene-therapeutic applications. It is likely that nature has leveraged this addressing system for gene regulation, because computational studies have uncovered an abundance of putative triplex target sites in various genomes, with enrichment particularly in gene promoters. However, to draw a more complete picture of the in vivo role of triplexes, not only the putative targets but also the sequences acting as the third strand and their capability to pair with the predicted target sites need to be studied. Here we present Triplexator, the first computational framework that integrates all aspects of triplex formation, and showcase its potential by discussing research examples for which the different aspects of triplex formation are important. We find that chromatin-associated RNAs have a significantly higher fraction of sequence features able to form triplexes than expected at random, suggesting their involvement in gene regulation. We furthermore identify hundreds of human genes that contain sequence features in their promoter predicted to be able to form a triplex with a target within the same promoter, suggesting the involvement of triplexes in feedback-based gene regulation. With focus on biotechnological applications, we screen mammalian genomes for high-affinity triplex target sites that can be used to target genomic loci specifically and find that triplex formation offers a resolution of ~1300 nt.

Indexed as

AlgorithmsAnimalsChromatinCircular DichroismComputational BiologyDNAGene Expression ProfilingGenetic LociGenome, HumanGenomicsHumansHydrogen BondingNucleic Acid ConformationOligonucleotidesPromoter Regions, GeneticRNA-Binding ProteinsChromatinDNAOligonucleotidesRNA-Binding Proteinstriplex DNA

Identifiers

PMID22550012
PMCPMC3396377
OpenAlexW2154624139

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.