MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Area of Science:

• Multi-omics data analysis
• Bioinformatics and computational biology
• Systems biology

Background:

• Omics data (transcriptomics, proteomics, lipidomics, metabolomics) are crucial for identifying biomarkers and understanding biological functions.
• Traditional analysis methods like principal component analysis (PCA) offer high-level summaries but limit detailed pattern exploration.
• Creating interactive visualizations for detailed omics data exploration typically requires significant programming expertise.

Purpose of the Study:

• To introduce MODE, a software tool designed to democratize the creation of interactive HTML displays for omics data.
• To enable researchers without advanced coding skills to build and share detailed, interactive visualizations of their omics datasets.
• To facilitate deeper scientific discovery by allowing exploration of complex patterns within multidimensional omics data.

Main Methods:

• Development of the MODE software application for generating interactive HTML displays.
• Implementation of features for plot design customization and meta-variable selection (categorical or numerical data for filtering/sorting).
• Demonstration of MODE's capabilities using lipidomics data from a plant leaf wounding study.

Main Results:

• MODE successfully empowers users to create shareable, interactive HTML displays of omics data.
• The software overcomes the coding barrier, making detailed data exploration accessible to a wider research community.
• Interactive displays allow for sorting and filtering data by various 'metas', revealing biologically relevant patterns.

Conclusions:

• MODE significantly lowers the barrier to entry for interactive omics data visualization and analysis.
• The tool enhances scientific discovery by enabling more intuitive and detailed exploration of complex biological data.
• MODE provides a versatile and accessible platform for sharing and interpreting multi-omics findings across different operating systems.