v1.0.0 Initial Release

v1.0.0 Release Notes

We are excited to announce the initial release of SEgene, a comprehensive platform for identifying and analyzing Super-Enhancer-to-gene links through statistical approaches. This release marks the foundation of a toolset that will enable researchers to explore gene regulation with greater insight.

Detailed Functionality

• peak_to_gene_links

  • Retrieves correlation information between gene expression and enhancer peaks.
  • Supports data analysis and visualization.

• SE_to_gene_links

  • Evaluates and analyzes super-enhancer to gene links using correlation data from peak_to_gene_links.
  • Utilizes graph theory for data visualization and interactive analysis with Jupyter Notebook.

For detailed installation instructions and documentation, please visit our GitHub repository.

Full Changelog: https://github.com/hamamoto-lab/SEgene/commits/v1.0.0