EPIXplorer: A Web Server for Prediction, Analysis and Visualization of Enhancer-Promoter Interactions

Authors

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

Language

English

Publication Date

7-5-2022

Journal

Nucleic Acids Research

DOI

10.1093/nar/gkac397

PMID

35639508

PMCID

PMC9252822

PubMedCentral® Posted Date

5-25-2022

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, Algorithms, Computers, Disease Susceptibility, Enhancer Elements, Genetic, Genome-Wide Association Study, Promoter Regions, Genetic, Regulatory Sequences, Nucleic Acid, Software, Internet

Published Open-Access

yes

Recommended Citation

Tang, Li; Zhong, Zhizhou; Lin, Yisheng; et al., "EPIXplorer: A Web Server for Prediction, Analysis and Visualization of Enhancer-Promoter Interactions" (2022). Children’s Nutrition Research Center Staff Publications. 316.
https://digitalcommons.library.tmc.edu/staff_pub/316