Evidence map›Paper›PMID 23034802›Full record

ArticleNucleic acids research2012

MiRmap: comprehensive prediction of microRNA target repression strength.

Charles E Vejnar, Evgeny M Zdobnov

Abstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 249 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
249citing 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

249 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. miRNA Target Prediction: An Overview of the Past and Current Tools.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  18. Predicting miRNA Targets at the isomiR Level.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  19. Article
  20. Article

189 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

2 authors.

Charles E VejnarDepartment of Genetic Medicine and Development, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland.
Evgeny M Zdobnov

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.

Indexed as

SoftwareBase PairingEvolution, MolecularGene Expression RegulationHeLa CellsHumansMicroRNAsProbabilityRNA, MessengerThermodynamicsMicroRNAsRNA, Messenger

Identifiers

PMID23034802
PMCPMC3526310

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.