A practical resource for multi-omics data integration in microbial systems

The increasing availability of microbial multi-omics datasets has created new opportunities to explore complex biological systems. However, exploration remains limited by the lack of accessible, reproducible workflows that integrate multiple omics layers and deliver easily interpretable visualizations of functional and pathway-level insights. Here, we present an R-based workflow for integrated analysis and network-based interpretation of microbial multi-omics data. The workflow enables microbiologists to analyse transcriptomic, proteomic and metabolomic datasets either individually or in combination, apply univariate and multivariate approaches for biomarker discovery and generate interpretable visualizations of functional and pathway-level signatures. Implemented as a multi-step R Markdown workflow, the resource leverages established open-source tools, including mixOmics for biomarker identification and omics integration and clusterProfiler for functional enrichment analysis. Functional integration is then performed on the enrichment results themselves rather than on individual features. By combining functional annotations from distinct omics layers, strains or conditions into a unified network, it enables simultaneous visualization of functional connectivity and of the sources from which each response was recovered, so that conserved and condition-specific responses can be identified directly rather than reconciled across separate outputs. A dedicated Shiny application makes this framework fully dynamic, allowing users to interactively filter, interpret and prioritize shared and condition-specific biological responses. We demonstrate its utility using a publicly available Streptococcus pyogenes dataset, revealing both shared and strain-specific functional responses to human serum. This resource provides an adaptable framework for systematic multi-omics analysis, improving accessibility and reproducibility and facilitating functional interpretation of microbial responses to diverse environments.