ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology

Area of Science:

• Systems Biology
• Bioinformatics
• Plant Science

Background:

• Systems biology research faces challenges integrating diverse data from separate sources.
• Current workflows increase cognitive load, hindering hypothesis generation.
• A unified platform for integrated data access, search, analysis, and visualization is needed.

Purpose of the Study:

• To present ePlant, a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data.
• To provide integrated search, analysis, and visualization features through a single portal.
• To facilitate hypothesis generation by reducing cognitive load in data exploration.

Main Methods:

• ePlant connects to public web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data.
• Data are visualized using a zoomable user interface and a conceptual hierarchy.
• The tool combines information from multiple data types within its visualization features.

Main Results:

• ePlant provides integrated access to diverse Arabidopsis thaliana data.
• The platform offers a user-friendly, zoomable interface for data exploration.
• Examples demonstrate ePlant's utility in generating hypotheses through integrative features.

Conclusions:

• ePlant addresses the need for a robust research platform in systems biology.
• The tool enhances hypothesis generation by simplifying complex data navigation.
• ePlant's code is freely available for adaptation to other biological species.