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March 6th, 2025
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Nantes Universite
systems biology
biorxiv

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

Pare, L.Open in Google Scholar•Bordron, P.Open in Google Scholar•David, L.Open in Google Scholar•Mahe, M.Open in Google Scholar•Bihouee, A.Open in Google Scholar•Eveillard, D.Open in Google Scholar

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.

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