Evidence map›Paper›PMID 19603121›Full record

ArticleMolecular bioSystems2009

Genome-wide survey of microRNA-transcription factor feed-forward regulatory circuits in human.

Angela Re, Davide Corá, Daniela Taverna, Michele Caselle

Open access · bronzeAbstract read
In one paragraph

Article in Molecular bioSystems, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

62 citing papers in PubMed, 100 citations in OpenAlex.

  1. Review
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  11. Molecular Filters for Noise Reduction.Biophysical journal · 2018
    Article
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  13. Review
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  15. Article
  16. Article
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  19. Article
  20. Article

2 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.

Angela ReCIBIO-Centre for Integrative Biology, University of Trento, I-38100 Trento, Italy. re@science.unitn.it
Davide Corá
Daniela Taverna
Michele Caselle
Accademia Albertina delle Belle Arti · ITUniversity of Trento · ITUniversity of Turin · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, we describe a computational framework for the genome-wide identification and characterization of mixed transcriptional/post-transcriptional regulatory circuits in humans. We concentrated in particular on feed-forward loops (FFL), in which a master transcription factor regulates a microRNA, and together with it, a set of joint target protein coding genes. The circuits were assembled with a two step procedure. We first constructed separately the transcriptional and post-transcriptional components of the human regulatory network by looking for conserved over-represented motifs in human and mouse promoters, and 3'-UTRs. Then, we combined the two subnetworks looking for mixed feed-forward regulatory interactions, finding a total of 638 putative (merged) FFLs. In order to investigate their biological relevance, we filtered these circuits using three selection criteria: (I) GeneOntology enrichment among the joint targets of the FFL, (II) independent computational evidence for the regulatory interactions of the FFL, extracted from external databases, and (III) relevance of the FFL in cancer. Most of the selected FFLs seem to be involved in various aspects of organism development and differentiation. We finally discuss a few of the most interesting cases in detail.

Indexed as

Computational BiologyGene Regulatory NetworksGenome, HumanGenomicsHumansMicroRNAsTranscription FactorsMicroRNAsTranscription Factors

Identifiers

PMID19603121
PMCPMC2898627
OpenAlexW2168251502

What OpenQuestion holds

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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.