Evidence map›Paper›PMID 27604961›Full record

ArticleRNA (New York, N.Y.)2016

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs.

Yu Guo, Katherine Alexander, Andrew G Clark, Andrew Grimson, Haiyuan Yu

Open access · bronzeAbstract read
In one paragraph

Article in RNA (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

14 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Coding and Non-coding RNAs: Molecular Basis of Forest-Insect Outbreaks.Frontiers in cell and developmental biology · 2020
    Article
  8. Review
  9. Review
  10. Article
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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

5 authors at 1 institution in 1 country.

Yu GuoWeill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA.
Katherine AlexanderDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA.
Andrew G ClarkDepartment of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853, USA.
Andrew GrimsonDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA.
Haiyuan YuWeill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA.
Cornell University · US

Funding

Identification and Functional Validation of Human Infertility AllelesR01HD082568 · NICHD · CORNELL UNIVERSITY · PI SCHIMENTI, JOHN C · 2015 to 2024
$6.0M
Whole Genome Sequencing to Discover Familial Myeloma Risk GenesR01CA167824 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, OFFIT, KENNETH · 2012 to 2016
$1.9M
Towards a comprehensive protein interactome network in Schizosaccharomyces pombeR01GM097358 · NIGMS · CORNELL UNIVERSITY · PI YU, HAIYUAN · 2012 to 2016
$1.7M
Using Protein Interactome Networks to Understand the Functional Role of Coding DNR01GM104424 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, YU, HAIYUAN · 2013 to 2016
$1.7M
Computational Methods for Unraveling Combinatorial Gene RegulationR01GM108716 · NIGMS · UNIVERSITY OF IOWA · PI TAN, KAI · 2014 to 2017
$1.2M
NCI NIH HHS R01 CA167824NICHD NIH HHS R01 HD082568NIGMS NIH HHS R01 GM097358NIGMS NIH HHS R01 GM104424NIGMS NIH HHS R01 GM108716
6 · The paper itself

Abstract

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

Indexed as

Gene Regulatory NetworksGene ExpressionMicroRNAsProteinsRNATranscription FactorsMicroRNAsProteinsRNATranscription Factorsbiological networksgene regulation

Identifiers

PMID27604961
PMCPMC5066619
OpenAlexW2512302398

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.