Evidence map›Paper›PMID 35639508›Full record

ArticleNucleic acids research2022

EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions.

Li Tang, Zhizhou Zhong, Yisheng Lin, Yifei Yang, Jun Wang, James F Martin, Min Li

Abstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. A 3D Genome Atlas of Genetic Variants and Their Pathological Effects in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  2. Article
  3. RAEPI: Predicting Enhancer-Promoter Interactions Based on Restricted Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2025
    Article
  4. Article
  5. Review
  6. 3D genomic organization in cancers.Quantitative biology (Beijing, China) · 2023
    Review
  7. Article
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

7 authors.

Li TangHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Zhizhou ZhongHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Yisheng LinHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Yifei YangHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Jun WangDepartment of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
James F MartinDepartment of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.
Min LiHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.ORCID 0000-0002-0188-1394

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long distance enhancers can physically interact with promoters to regulate gene expression through formation of enhancer-promoter (E-P) interactions. Identification of E-P interactions is also important for profound understanding of normal developmental and disease-associated risk variants. Although the state-of-art predictive computation methods facilitate the identification of E-P interactions to a certain extent, currently there is no efficient method that can meet various requirements of usage. Here we developed EPIXplorer, a user-friendly web server for efficient prediction, analysis and visualization of E-P interactions. EPIXplorer integrates 9 robust predictive algorithms, supports multiple types of 3D contact data and multi-omics data as input. The output from EPIXplorer is scored, fully annotated by regulatory elements and risk single-nucleotide polymorphisms (SNPs). In addition, the Visualization and Downstream module provide further functional analysis, all the output files and high-quality images are available for download. Together, EPIXplorer provides a user-friendly interface to predict the E-P interactions in an acceptable time, as well as understand how the genome-wide association study (GWAS) variants influence disease pathology by altering DNA looping between enhancers and the target gene promoters. EPIXplorer is available at https://www.csuligroup.com/EPIXplorer.

Indexed as

Genome-Wide Association StudyRegulatory Sequences, Nucleic AcidSoftwareAlgorithmsComputersDisease SusceptibilityEnhancer Elements, GeneticHumansInternetPromoter Regions, Genetic

Identifiers

PMID35639508
PMCPMC9252822

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.