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Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations
Suresh K Bhavnani1, Arunkumaar Ganesan, Theodore Hall
1Center for Computational Medicine & Bioinformatics, 2017 Palmer Commons Bldg,, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA. skbhavnani@gmail.com.
BMC Research Notes
|November 13, 2010
Summary
Three-dimensional (3D) network layouts revealed a new global relationship in renal disease gene regulation. Disease-specific genes were up-regulated, while non-specific genes were down-regulated, offering insights into common mechanisms across kidney diseases.
Area of Science:
- Bioinformatics
- Genomics
- Systems Biology
Background:
- Previous studies utilized 2D network layouts to analyze chronic renal diseases and gene regulation.
- Complex relationships were identified between disease type, gene specificity, and gene regulation, offering initial insights into biological pathways.
Purpose of the Study:
- To extend understanding of chronic renal disease and gene regulation by reanalyzing data using 3D network layouts.
- To explore the utility of 3D visualization in uncovering biological network regularities.
Main Methods:
- Reanalysis of existing data using three-dimensional (3D) network layouts.
- Visualization of 3D networks through both 2D and 3D viewing methods.
- Quantitative validation of findings through comparison with 1000 random permutations.
Main Results:
- 3D network analysis revealed that disease-specific genes (implicated in few diseases) were predominantly up-regulated.
- Non-specific genes (implicated in many diseases) were predominantly down-regulated.
- This global relationship was statistically validated, highlighting the effectiveness of 3D visualization.
Conclusions:
- A global relationship between gene regulation and specificity suggests common mechanisms across renal diseases.
- 3D visualization facilitated the detection of a regularity previously obscured in 2D representations.
- Future research should investigate the application of 3D network visualization for biological network analysis.
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