SEGCOND

SEGCOND predicts genomic regions associated with transcriptional condensates by integrating multi-omics data and Hi-C to identify enhancer-rich three-dimensional loci that influence gene regulation.

Key Features:

• Integration of Multi-Omics Data: Integrates genomic datasets related to enhancer activity and chromatin accessibility to support comprehensive genome segmentation.
• Genome Segmentation and Detection: Performs genome segmentation on integrated data to detect transcriptionally active regions.
• Hi-C Data Utilization: Incorporates Hi-C data to map 3D genomic interactions and identify putative transcriptional condensates (PTCs) formed by enhancer coalescence.
• Identification of Active Enhancer Segments: Highlights enhancer segments exhibiting increased transcriptional activity to infer regulatory roles.
• Application to Biological Systems: Applied to B-cell to macrophage transdifferentiation, revealing previously unreported genes implicated in that process.

Scientific Applications:

• Transcriptional Condensate Characterization: Identifying and characterizing transcriptional condensates and enhancer coalescence within 3D chromatin space.
• Enhancer Dynamics and Chromatin Architecture: Analyzing enhancer activity and chromatin architecture to study their roles in gene regulation.
• Cellular Differentiation and Disease: Investigating regulatory changes during cellular differentiation (e.g., B-cell to macrophage transdifferentiation) and disease-relevant states.
• Association with Super-Enhancers: Linking putative transcriptional condensates to super-enhancers identified by independent methods.

Methodology:

Combines multi-omics datasets related to enhancer activity and chromatin accessibility; performs genome segmentation to detect transcriptionally active regions; and integrates Hi-C data to identify 3D coalescence of enhancers and pinpoint putative transcriptional condensates (PTCs).