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SEanalysis 2.0: a comprehensive super-enhancer regulatory network analysis tool for human and mouse.
Feng-Cui Qian1,2,3,4,5,6, Li-Wei Zhou7, Yan-Yu Li7
1The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Nucleic Acids Research
|May 17, 2023
Summary
SEanalysis 2.0 enhances the study of super-enhancers (SEs) and transcription factors (TFs) by expanding human and mouse data. This updated web server offers new analysis tools for deeper insights into gene regulation and disease associations.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Super-enhancers (SEs) are crucial regulatory elements involved in biological processes and diseases.
- Their interactions with transcription factors (TFs) dictate gene expression patterns.
- Understanding these complex regulatory networks is vital for biological and medical research.
Purpose of the Study:
- To introduce SEanalysis 2.0, an upgraded web server for comprehensive analysis of SE-centric regulatory networks.
- To expand the dataset with human and mouse SEs and introduce novel analytical functionalities.
- To facilitate in-depth understanding of context-specific gene regulation and SE-related disease mechanisms.
Main Methods:
- Expanded human and mouse SE datasets, documenting over 1.1 million human SEs and over 550,000 mouse SEs.
- Integrated new analysis modules: 'TF regulatory analysis' and 'Sample comparative analysis'.
- Annotated risk Single Nucleotide Polymorphisms (SNPs) to SE regions for disease association studies.
Main Results:
- SEanalysis 2.0 significantly increases the scale of SE data compared to its predecessor.
- Enhanced existing analysis tools ('pathway downstream analysis', 'upstream regulatory analysis', 'genomic region annotation') for improved context-specific gene regulation insights.
- Introduced novel analyses for TF-driven SE networks and comparative sample analysis, alongside SNP annotation for disease relevance.
Conclusions:
- SEanalysis 2.0 provides substantially expanded data and analytical capabilities for SE research.
- The updated platform aids researchers in dissecting complex SE regulatory networks.
- This resource facilitates a deeper understanding of SE functions in biological regulation and disease pathogenesis.
