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

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...

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Detalles Bibliográficos
Autores principales: Tang, Li, Zhong, Zhizhou, Lin, Yisheng, Yang, Yifei, Wang, Jun, Martin, James F, Li, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252822/
https://www.ncbi.nlm.nih.gov/pubmed/35639508
http://dx.doi.org/10.1093/nar/gkac397
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author Tang, Li
Zhong, Zhizhou
Lin, Yisheng
Yang, Yifei
Wang, Jun
Martin, James F
Li, Min
author_facet Tang, Li
Zhong, Zhizhou
Lin, Yisheng
Yang, Yifei
Wang, Jun
Martin, James F
Li, Min
author_sort Tang, Li
collection PubMed
description 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.
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spelling pubmed-92528222022-07-05 EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions Tang, Li Zhong, Zhizhou Lin, Yisheng Yang, Yifei Wang, Jun Martin, James F Li, Min Nucleic Acids Res Web Server Issue 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. Oxford University Press 2022-05-25 /pmc/articles/PMC9252822/ /pubmed/35639508 http://dx.doi.org/10.1093/nar/gkac397 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Tang, Li
Zhong, Zhizhou
Lin, Yisheng
Yang, Yifei
Wang, Jun
Martin, James F
Li, Min
EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title_full EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title_fullStr EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title_full_unstemmed EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title_short EPIXplorer: A web server for prediction, analysis and visualization of enhancer-promoter interactions
title_sort epixplorer: a web server for prediction, analysis and visualization of enhancer-promoter interactions
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252822/
https://www.ncbi.nlm.nih.gov/pubmed/35639508
http://dx.doi.org/10.1093/nar/gkac397
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