HIPPIE: a high-throughput identification pipeline for promoter interacting enhancer elements.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Understanding enhancer-promoter interactions is crucial for deciphering gene regulation.
• Existing methods for identifying these interactions can be complex and time-consuming.
• High-throughput sequencing technologies like Hi-C provide valuable data for studying 3D genome organization.

Purpose of the Study:

• To develop and implement a high-throughput computational pipeline for identifying promoter-interacting enhancer elements.
• To streamline the workflow from raw Hi-C data processing to the extraction of enhancer-target gene relationships.
• To facilitate the discovery of both intra- and inter-chromosomal regulatory interactions.

Main Methods:

• Implementation of a novel high-throughput identification pipeline for promoter interacting enhancer elements.
• Utilized Hi-C sequencing data for mapping DNA-DNA interacting fragments with high confidence.
• Integrated histone modification and DNase hypersensitive site enrichment data to identify putative enhancer elements.

Main Results:

• Successfully established a streamlined workflow for analyzing Hi-C data.
• Enabled high-confidence identification of DNA-DNA interacting fragments.
• Facilitated the extraction of potential intra- and inter-chromosomal enhancer-target gene relationships.

Conclusions:

• The developed pipeline significantly enhances the efficiency of identifying enhancer-promoter interactions.
• This tool provides a robust framework for exploring gene regulatory networks.
• The freely available software promotes accessibility for academic and non-profit research.