A practical resource for multi-omics data integration in microbial systems
4 affiliations
- 1Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- 2Centre for Pathogen Genomics, The University of Melbourne, Melbourne, Victoria, Australia
- 3Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, 3000, Australia
- 4Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, Doherty Institute, University of Melbourne, Melbourne, Victoria, Australia
Abstract
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.
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BibTeX
@article{mujchariyakul2026practical,
title = {{A practical resource for multi-omics data integration in microbial systems}},
author = {Warasinee Mujchariyakul and Abderrahman Hachani and Timothy P. Stinear and Kim-Anh Lê Cao and Benjamin P. Howden and Calum J. Walsh and Romain Guérillot},
journal = {Microbial Genomics},
year = {2026},
month = nov,
doi = {10.1099/mgen.0.001860},
url = {https://doi.org/10.1099/mgen.0.001860},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A practical resource for multi-omics data integration in microbial systems
AU - Warasinee Mujchariyakul
AU - Abderrahman Hachani
AU - Timothy P. Stinear
AU - Kim-Anh Lê Cao
AU - Benjamin P. Howden
AU - Calum J. Walsh
AU - Romain Guérillot
PY - 2026
DA - 2026/11/04
JO - Microbial Genomics
DO - 10.1099/mgen.0.001860
UR - https://doi.org/10.1099/mgen.0.001860
AB - 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.
SN - 2057-5858
ER -