SEanalysis

SEanalysis characterizes transcriptional regulatory networks centering on super-enhancers (SEs) by integrating SEs, transcription factors (TFs), genes, pathways, and SNP annotations in human and mouse.

Key Features:

• Extensive SE dataset: Includes 1,167,518 human SEs derived from 1,739 samples and 550,226 mouse SEs from 931 samples, representing a dataset more than five times larger than version 1.0.
• Pathway Downstream Analysis: Investigates downstream pathways influenced by SEs.
• Upstream Regulatory Analysis: Examines upstream regulatory factors that modulate SE activity.
• Genomic Region Annotation: Provides genomic annotations for regions associated with SEs.
• TF Regulatory Analysis: Analyzes regulatory roles of transcription factors within SE networks.
• Sample Comparative Analysis: Enables comparative analyses across samples to identify context-specific SE regulatory patterns.
• Disease and Trait Annotation: Annotates risk single nucleotide polymorphisms (SNPs) to SE regions to link SEs with disease- or trait-associated variants.

Scientific Applications:

• Gene regulation and TF interactions: Profiles SE-mediated control of gene expression and TF–SE interactions.
• Pathway analysis: Links SEs to downstream pathways to study biological processes.
• Comparative genomics: Compares SE regulatory landscapes between human and mouse samples.
• Genetic studies of disease and traits: Maps risk SNPs to SEs to investigate genetic contributions to disease susceptibility and trait variation.

Methodology:

Compilation of SE collections (1,167,518 human SEs from 1,739 samples; 550,226 mouse SEs from 931 samples) and application of analytical models explicitly stated as Pathway Downstream Analysis, Upstream Regulatory Analysis, Genomic Region Annotation, TF Regulatory Analysis, Sample Comparative Analysis, and annotation of risk single nucleotide polymorphisms (SNPs) to SE regions.