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BiomiX: Making Multi-Omics Analysis Accessible to All Scientists

In modern biological research, interpreting changes in complex biological systems often requires analyzing vast amounts of data from multiple “omics” layers—genomics, transcriptomics, metabolomics, and methylomics. While tools for analyzing single-omics data have become increasingly user-friendly, integrating multiple omics datasets remains a challenge that often demands advanced bioinformatics expertise. This technical barrier can limit the broader scientific community from fully leveraging the potential of multi-omics studies.

Enter BiomiX, a standalone R-based tool designed to bridge this gap and make high-throughput multi-omics analysis accessible to researchers without extensive computational training.

What BiomiX Offers

BiomiX is engineered to handle multi-omics data from two cohorts, integrating diverse datasets into a coherent framework for analysis and visualization. Here's how it works:

  • Transcriptomics Analysis: Leveraging popular R packages like DESeq2 and Limma, BiomiX identifies differential gene expression efficiently.

  • Metabolomics Analysis: It quantifies metabolite peak differences using the Wilcoxon test with False Discovery Rate (FDR) correction. Metabolites from untargeted LC-MS experiments are annotated using the CEU Mass Mediator database and fragmentation spectra processed through TidyMass.

  • Methylomics Analysis: BiomiX uses the ChAMP package to evaluate DNA methylation changes.

The true power of BiomiX lies in its ability to integrate these layers through Multi-Omics Factor Analysis (MOFA). MOFA identifies shared sources of variation across datasets, enabling a comprehensive view of biological processes and uncovering patterns that single-omics analysis alone might miss.

From Data to Insights

BiomiX doesn’t stop at raw analysis. It provides:

  • Statistical reports and publication-ready figures

  • Functional exploration via EnrichR and GSEA, supporting subgroup analyses based on user-defined gene panels

  • MOFA fine-tuning to optimize factor selection, distinguish between cohorts, and identify discriminative factors

One of the standout features of BiomiX is its literature integration. Using PubMed, the tool retrieves relevant articles related to discriminant MOFA factors, helping researchers interpret their results in the context of existing scientific knowledge. Moreover, BiomiX correlates MOFA factors with clinical data, highlighting the most significant contributing pathways and aiding in biological interpretation.

Why BiomiX Matters

BiomiX is designed with the principles of FAIR data (Findable, Accessible, Interoperable, Reusable) in mind. Its interactive visualizations, customizable parameters, and multi-platform compatibility make it a versatile tool for both bioinformatics experts and non-experts alike. By democratizing multi-omics analysis, BiomiX enables:

  • Efficient exploration of complex biological datasets

  • Improved biological insight and hypothesis generation

  • Enhanced accessibility for researchers across disciplines

Conclusion

The field of multi-omics is rapidly advancing, but tools that integrate and interpret diverse data types in an accessible way have been scarce—until now. BiomiX provides a comprehensive, user-friendly platform for integrated multi-omics analysis, bridging the gap between complex datasets and meaningful biological insight. Whether you’re investigating transcriptomics, metabolomics, methylomics, or their integration, BiomiX empowers scientists to uncover new discoveries without requiring advanced programming skills.