Visualization of genomic data with the Hilbert curve

Area of Science:

• Genomics
• Bioinformatics
• Data Visualization

Background:

• Genomic studies often involve complex genome-position-dependent data, such as ChIP-chip and ChIP-Seq scores.
• Conventional tools can hinder comprehensive understanding of data distributions and feature patterns.

Purpose of the Study:

• To introduce Hilbert curve visualization as a complementary approach for analyzing genomic data.
• To demonstrate how this visualization aids in gaining deeper insights into data structures.
• To present an open-source application, HilbertVis, for creating and exploring these plots.

Main Methods:

• Utilizing Hilbert curves to map and visualize genome-position-dependent data.
• Developing an interactive, open-source application (HilbertVis) for generating these visualizations.
• Applying the method to diverse use cases in genomic data analysis.

Main Results:

• Hilbert curve visualization effectively complements existing genome browsers.
• The approach reveals patterns and distributions not easily discernible with conventional tools.
• HilbertVis provides an interactive platform for exploring genomic data structures.

Conclusions:

• Hilbert curve visualization is a valuable tool for enhancing the analysis of genomic data.
• The HilbertVis application facilitates interactive exploration and deeper understanding of genomic datasets.
• This method offers new perspectives for interpreting complex genomic information.