OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genomic sequencing generates vast amounts of annotation data.
• Integrating genomic annotations with specific regions (e.g., genes) can reveal insights into gene regulation.
• Manual data integration is complex, time-consuming, and error-prone, while semi-automatic methods require programming expertise.

Purpose of the Study:

• To develop an automated tool for associating and visualizing genomic regions with annotations.
• To provide a user-friendly solution for researchers lacking bioinformatic training.
• To streamline the analysis of genomic data and uncover novel associations.

Main Methods:

• Developed Overlapping Annotated Genomic Regions (OGRE), a tool for parsing input regions and mining public annotations.
• Implemented algorithms for calculating overlaps between genomic regions and annotations.
• Integrated visualization tools for presenting results, including location, type, and number of regulatory elements.

Main Results:

• OGRE successfully associates and visualizes input regions with genomic annotations.
• The tool provides clear visualizations and result tables, identifying regulatory elements.
• Applied OGRE to recent studies, demonstrating high reproducibility and potential for new discoveries.
• Benchmarking confirmed OGRE's competitive performance and additional features compared to similar tools.

Conclusions:

• OGRE serves as a downstream analysis step compatible with most genomic sequencing outputs.
• The tool enriches existing analysis pipelines by automating overlap calculations and visualization.
• OGRE offers an end-to-end solution for genomic data analysis, benefiting both biologists and computational scientists.
• It addresses the need for accessible tools in genomic data interpretation.