Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput sequencing generates vast omics data, crucial for life science research.
• Existing R packages (e.g., DESeq2, clusterProfiler) and online tools aid omics analysis but offer limited chart customization.
• Current tools often produce fixed, non-editable charts, hindering detailed data representation.

Purpose of the Study:

• To develop Visual Omics, an integrated online platform for omics data analysis and scientific chart editing.
• To provide bench scientists with a user-friendly tool for comprehensive omics data exploration and visualization.
• To enable iterative refinement of scientific charts to meet publication standards.

Main Methods:

• Integration of multiple omics analyses: differential expression, enrichment, protein domain prediction, and protein-protein interaction.
• Implementation of extensive graph presentations and customization options for basic omics charts (PCA/PCoA, heat maps, volcano plots, etc.).
• Development as an online tool accessible via a web interface, requiring no programming expertise.

Main Results:

• Visual Omics provides a one-stop solution for omics data analysis and visualization.
• Users can perform analyses, explore graph layouts, and fine-tune parameters interactively online.
• The tool generates publication-ready, customizable scientific charts from omics data.

Conclusions:

• Visual Omics empowers researchers by simplifying complex omics data analysis and visualization.
• The platform facilitates the creation of clear, detailed, and publication-quality scientific figures.
• It addresses the limitations of existing tools by offering integrated analysis and flexible chart editing capabilities.