Software Article | Open Access

Volume 2026 |Article ID 100131 | https://doi.org/10.1016/j.plaphe.2025.100131

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

Ankur Sahu,¹ Dennis Psaroudakis,^1,2 Hardy Rolletschek,¹ Kerstin Neumann,¹ Ljudmilla Borisjuk,¹ Axel Himmelbach,¹ Kalyan Pinninti,¹ Dominic Knoch,¹ Nadine Topfer,³ Jędrzej Szymanski ^1,2

¹Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
²Forschungszentrum Jülich, Institute of Bio- and Geosciences (IBG-4 Bioinformatics), CEPLAS, BioSC, Jülich, Germany
³Institute for Plant Sciences, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Zülpicher Str. 47b, 50674, Cologne, Germany

Abstract

Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.