Evidence map›Paper›PMID 34570239›Full record

ArticleNucleic acids research2021

Predict long-range enhancer regulation based on protein-protein interactions between transcription factors.

Hao Wang, Binbin Huang, Jianrong Wang

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

20 citing papers in PubMed, 26 citations in OpenAlex.

  1. Genome evolution through polyploidy: Enhancing plant stress resilience in agriculture.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Review
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  4. EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.International journal of molecular sciences · 2025
    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

3 authors at 1 institution in 1 country.

Hao WangDepartment of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA.
Binbin HuangDepartment of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA.
Jianrong WangDepartment of Computational Mathematics, Science and Engineering, Michigan State University, 428 S. Shaw Ln., East Lansing, MI 48824, USA.ORCID 0000-0002-9290-4888
Michigan State University · US

Funding

Statistical modeling of long-range chromatin interactions on gene regulation and underlying molecularR01GM131398 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WANG, JIANRONG · 2018 to 2021
$1.3M
NIGMS NIH HHS R01 GM131398
6 · The paper itself

Abstract

Long-range regulation by distal enhancers plays critical roles in cell-type specific transcriptional programs. Computational predictions of genome-wide enhancer-promoter interactions are still challenging due to limited accuracy and the lack of knowledge on the molecular mechanisms. Based on recent biological investigations, the protein-protein interactions (PPIs) between transcription factors (TFs) have been found to participate in the regulation of chromatin loops. Therefore, we developed a novel predictive model for cell-type specific enhancer-promoter interactions by leveraging the information of TF PPI signatures. Evaluated by a series of rigorous performance comparisons, the new model achieves superior performance over other methods. The model also identifies specific TF PPIs that may mediate long-range regulatory interactions, revealing new mechanistic understandings of enhancer regulation. The prioritized TF PPIs are associated with genes in distinct biological pathways, and the predicted enhancer-promoter interactions are strongly enriched with cis-eQTLs. Most interestingly, the model discovers enhancer-mediated trans-regulatory links between TFs and genes, which are significantly enriched with trans-eQTLs. The new predictive model, along with the genome-wide analyses, provides a platform to systematically delineate the complex interplay among TFs, enhancers and genes in long-range regulation. The novel predictions also lead to mechanistic interpretations of eQTLs to decode the genetic associations with gene expression.

Indexed as

AnimalsComputational BiologyEnhancer Elements, GeneticGene Expression RegulationHumansPromoter Regions, GeneticProtein BindingTranscription FactorsTranscription Factors

Identifiers

PMID34570239
PMCPMC8501976
OpenAlexW3203989237

What Socratic holds

Textmetadata
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 Socratic graph.