panomiX: Investigating mechanisms of trait emergence through multi-omics data integration

Area of Science:

• Plant biology
• Computational biology
• Genomics

Background:

• Integrating diverse omics data (genomics, metabolomics, phenomics) with high-throughput phenotyping is challenging due to data complexity.
• Linking molecular signatures to plant traits requires advanced computational methods often inaccessible to non-experts.

Purpose of the Study:

• To introduce panomiX, a user-friendly toolbox for multi-omics data integration.
• To enable non-experts to apply advanced computational methods for analyzing plant molecular and phenotypic data.
• To identify condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits in plants.

Main Methods:

• PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling using machine learning.
• The toolbox was applied to a tomato heat-stress experiment integrating image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data.
• Network analysis was employed to reveal interactions between molecular data and phenotypic traits.

Main Results:

• PanomiX successfully integrated multi-omics data, revealing meaningful molecular interactions and synergies.
• A network of condition-specific, cross-domain connections was identified in tomato under heat stress.
• Significant links were found between photosynthesis traits and stress-responsive kinases at elevated temperatures.

Conclusions:

• PanomiX simplifies complex multi-omics analyses, enhancing interpretability for researchers.
• The toolbox accelerates the discovery of plant trait emergence and facilitates the selection of candidate genes.
• PanomiX provides a valuable platform for advancing plant science through integrated omics approaches.