HUMESS: Integrating Quantitative Transcriptomic Analysis and Metabolic Modeling to Unveil Condition-Specific Gene Signatures

Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that integrates transcriptomic data with metabolic network modeling to address this. Unlike traditional methods, HUMESS prioritizes genes based on their metabolic significance, offering a deeper understanding of condition-specific gene expression. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures.