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SEdb 3.0: a comprehensive super-enhancer database across multiple species.

Shuang Song1,2, Liyuan Liu1,2, Chenchen Feng3

  • 1The First Affiliated Hospital and Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

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|December 8, 2025
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Summary
This summary is machine-generated.

Super-enhancers (SEs) are crucial for cell identity. SEdb 3.0 expands this resource with millions of SEs across species, adding enhancer RNAs and cofactor binding sites for deeper biological insights.

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Super-enhancers (SEs) are critical cis-regulatory elements controlling cell-specific gene expression and identity.
  • Existing databases offer limited SE information, hindering comprehensive analysis across diverse species and regulatory mechanisms.

Purpose of the Study:

  • To present SEdb 3.0, a significantly expanded and updated resource for SEs and their regulatory annotations.
  • To enhance the scale and utility of SEdb for research in humans, model organisms, and plants.
  • To provide new functional annotations and analysis tools for deeper investigation of SE functions.

Main Methods:

  • Curated 3,478,186 SEs from 5,387 H3K27ac ChIP-seq samples across four species (human, mouse, Arabidopsis thaliana, maize).
  • Integrated diverse epigenomic features: enhancer RNAs (eRNAs), transcription cofactor (TcoF) binding sites, and chromatin regulators (CRs).
  • Expanded existing annotations, including transcription factor (TF) ChIP-seq data, TF motifs, and SE-associated eQTL-gene regulatory pairs.

Main Results:

  • SEdb 3.0 offers a two-fold expansion in human and mouse SE entries and introduces plant SE data.
  • New annotations provide insights into SE transcriptional activity (eRNAs), regulatory mechanisms (TcoF binding), and chromatin states (CRs).
  • Substantial increases in TF ChIP-seq data, TF motifs, and eQTL associations, alongside new gene-SE association inference strategies and analysis tools.

Conclusions:

  • SEdb 3.0 represents a major upgrade, providing a comprehensive and user-friendly platform for SE research.
  • The expanded dataset and enhanced annotations facilitate detailed investigations into SE biological significance and regulatory roles.
  • This resource significantly advances the study of SEs in both animal and plant kingdoms.