Evidence map›Paper›PMID 25077573›Full record

ArticleBMC bioinformatics2014

Identifying microRNA targets in different gene regions.

Wenlong Xu, Anthony San Lucas, Zixing Wang, Yin Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 2 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

78 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Association ofRadiology and oncology · 2023
    Trial
  4. Review
  5. Review
  6. MicroRNAs in Tissue Regeneration: Lessons from Animal Models.International journal of molecular sciences · 2025
    Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. ESOMIR: a curated database of biomarker genes and miRNAs associated with esophageal cancer.Database : the journal of biological databases and curation · 2023
    Article
  20. Review

18 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.

Wenlong Xu
Anthony San Lucas
Zixing Wang
Yin Liu

Funding

Bayesian Methods in Signal Transduction Network AnalysisR01LM010022 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI LIU, YIN · 2011 to 2014
$901k
6 · The paper itself

Abstract

backgroundCurrently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.

resultsWe have developed TargetS, a novel computational approach for predicting miRNA targets with the target sites located along entire gene sequences, which permits finding additional targets that are not located in the 3' un-translated regions. Our model is based on both canonical seed matching and non-canonical seed pairing, which discovers targets that allow one bit GU wobble. It does not rely on evolutionary conservation, so it allows the detection of species-specific miRNA-mRNA interactions and makes it suitable for analyzing un-conserved gene sequences. To test the performance of our approach, we have imported the widely used benchmark dataset revealing fold-changes in protein production corresponding to each of the five selected microRNAs. Compared to well-known miRNA target prediction tools, including TargetScanS, PicTar and MicroT_CDS, our method yields the highest sensitivity, while achieving a comparable level of accuracy. Human miRNA target predictions using our computational approach are available online at http://liubioinfolab.org/targetS/mirna.html

conclusionsA simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites. Our method also expands the target search space to different gene regions, rather than limiting to 3'UTR only. This improves the sensitivity of miRNA target identification, while achieving a comparable accuracy with existing methods.

Indexed as

AlgorithmsGenomicsHumansMicroRNAsProtein BiosynthesisProteinsRegulatory Sequences, Nucleic AcidRNA, MessengerMicroRNAsProteinsRNA, Messenger

Identifiers

PMID25077573
PMCPMC4110731

What OpenQuestion holds

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LicenceCC BY
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.