O'Loughlin, J.; Moses, T.

Metabolomics offers a sophisticated analytical framework for characterising the molecular phenotype of biological organisms and complex living systems at a high resolution. As the functional endpoint of the omics cascade, the metabolome serves as a close reflection of cellular activity. It integrates genetic, transcriptomic and proteomic variations with external environmental influences. However, the inherent complexity of metabolomic datasets, characterised by high-dimensional chemical diversity, wide dynamic ranges, and significant matrix effects, necessitates a rigorous suite of chemometric and bioinformatic workflows. For researchers uninitiated in computational biology, the multi-stage requirement for raw data pre-processing, signal deconvolution, and multivariate statistical modelling (such as PCA or PLS-DA) presents a substantial barrier to entry. Navigating these convoluted data architectures remains a primary challenge in deriving biological meaning from the global metabolic profile. Here, we present a workflow to use Python Dash Apps to create a user-friendly interface for simplifying data processing and statistical calculations. Users can select their desired samples to initiate calculations for various statistical tests, generating interactive and publication-quality figures to explore their results. These apps were deployed on an Apache server via cPanel, allowing individuals to share their findings with collaborators and for research facilities to share metabolomics results with their users.