BiomiX, a User-Friendly Bioinformatic Tool for Automatized Multiomics Data Analysis and Integration

BiomiX addresses the data analysis bottleneck in high-throughput omics technologies, enabling the efficient, integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data. DESeq2/Limma packages analyze transcriptomics data, while statistical tests determine metabolomics peaks. The metabolomics annotation uses the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package while Methylomics analysis is performed using the ChAMP R package. Multiomics Factor Analysis (MOFA) integration and interpretation identifies common sources of variations among omics. BiomiX provides comprehensive outputs, including statistics and report figures, also integrating EnrichR and GSEA for biological process exploration. Subgroup analysis based on user gene panels enhances comparisons. BiomiX implements MOFA automatically, selecting the optimal MOFA model to discriminate the two cohorts being compared while providing interpretation tools for the discriminant MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. The interpretation is also supported by clinical data correlation with the discriminant MOFA factors and pathways analyses of the top factor contributors. The integration of single and multi-omics analysis in a standalone tool, together with the implementation of MOFA and its interpretability by literature, constitute a step forward in the multi-omics landscape in line with the FAIR data principles. The wide parameter choice grants a personalized analysis at each level based on the user requirements. BiomiX is a user-friendly R-based tool compatible with various operating systems that aims to democratize multiomics analysis for bioinformatics non-experts. Key pointsO_LIBiomiX is the first user-friendly multiomics tool to perform single omics analysis for transcriptomics, metabolomics and methylomics and their data integration by MOFA in the same platform. C_LIO_LIMOFA algorithm was made accessible to non-bioinformaticians and improved to select the best model automatically, testing the MOFA factors performance in groups separation. C_LIO_LILarge improvement of MOFA factors interpretability by correlation, pathways analysis and innovative bibliography research. C_LIO_LIBiomiX is embedded in a network of other online tools as GSEA, metaboanalyst EnrichR etc, to provide a format compatible with further analyses in these tools. C_LIO_LIInterface and usage are intuitive and compatible with all the main operating systems, and rich parameters are set to grant personalized analysis based on the users needs. C_LI