SEanalysis 2.0: a comprehensive super-enhancer regulatory network analysis tool for human and mouse

Author:

Qian Feng-Cui123456,Zhou Li-Wei7,Li Yan-Yu7,Yu Zheng-Min1,Li Li-Dong4,Wang Yue-Zhu7,Xu Ming-Cong1,Wang Qiu-Yu123456,Li Chun-Quan123456ORCID

Affiliation:

1. The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China , Hengyang , Hunan  421001, China

2. The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China , Hengyang , Hunan  421001, China

3. National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital , Changsha , Hunan  410008, China

4. School of Computer, University of South China , Hengyang , Hunan  421001, China

5. Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China , Hengyang , Hunan  421001, China

6. The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China , Hengyang , China

7. School of Medical Informatics, Daqing Campus, Harbin Medical University , Daqing  163319, China

Abstract

Abstract Super-enhancers (SEs) play an essential regulatory role in various biological processes and diseases through their specific interaction with transcription factors (TFs). Here, we present the release of SEanalysis 2.0 (http://licpathway.net/SEanalysis), an updated version of the SEanalysis web server for the comprehensive analyses of transcriptional regulatory networks formed by SEs, pathways, TFs, and genes. The current version added mouse SEs and further expanded the scale of human SEs, documenting 1 167 518 human SEs from 1739 samples and 550 226 mouse SEs from 931 samples. The SE-related samples in SEanalysis 2.0 were more than five times that in version 1.0, which significantly improved the ability of original SE-related network analyses (‘pathway downstream analysis’, ‘upstream regulatory analysis’ and ‘genomic region annotation’) for understanding context-specific gene regulation. Furthermore, we designed two novel analysis models, ‘TF regulatory analysis’ and ‘Sample comparative analysis’ for supporting more comprehensive analyses of SE regulatory networks driven by TFs. Further, the risk SNPs were annotated to the SE regions to provide potential SE-related disease/trait information. Hence, we believe that SEanalysis 2.0 has significantly expanded the data and analytical capabilities of SEs, which helps researchers in an in-depth understanding of the regulatory mechanisms of SEs.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Heilongjiang Province

Research Foundation of the First Affiliated Hospital of University of South China for Advanced Talents

Research Foundation of Education Bureau of Hunan Province

Publisher

Oxford University Press (OUP)

Subject

Genetics

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