EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization

Area of Science:

• Quantitative genetics and bioinformatics
• Population genomics
• Multi-omics data analysis

Background:

• The increasing volume of population-scale omics data (genomics, transcriptomics, proteomics, metabolomics) presents challenges for wet-lab biologists.
• Traditional bioinformatics tools often require advanced programming and command-line expertise, creating a barrier to entry.

Purpose of the Study:

• To develop a user-friendly bioinformatics tool for population-scale omics data analysis.
• To enable wet-lab biologists to perform complex analyses like association, integration, and visualization of multi-omics datasets.
• To provide a platform-independent solution for accessible omics data exploration.

Main Methods:

• Development of EasyOmics, a stand-alone R Shiny application.
• Integration of functions for data quality control, heritability estimation, and various association analyses (GWAS, conditional, omics-wide).
• Implementation of omics quantitative trait locus (QTL) mapping and multi-omics data integration capabilities.
• Inclusion of visualization tools for generating publication-quality graphs through a point-and-click interface.
• Provision of a Docker container for simplified installation and cross-platform compatibility.

Main Results:

• EasyOmics offers a comprehensive suite of tools for population-scale omics data analysis.
• The application facilitates association studies, omics data integration, and visualization with an intuitive graphical user interface.
• Publication-quality figures can be generated easily, enhancing data interpretation and dissemination.
• The software is platform-independent and readily installable via Docker.

Conclusions:

• EasyOmics empowers wet-lab biologists to conduct sophisticated population-scale omics analyses.
• The tool democratizes access to advanced bioinformatics methods, bridging the gap between biological research and computational analysis.
• EasyOmics supports efficient exploration and interpretation of big omics data for genetic research.