TurboOmics: a web-based platform for the analysis of transcriptomics, proteomics and untargeted metabolomics data using a multi-Omics integrative approach

Multi-omics integrative analysis is an emerging field that allows for a deeper and more comprehensive profiling of interactions among molecular entities across various functional levels. Untargeted metabolomics holds particular promise in this context due to its unbiased approach and its ability to capture the dynamic interactions of the metabolome with the proteome and genome. However, the integration of these omics datasets is often hindered by the lack of tools that combine putative annotations of metabolomic features with multivariate statistical techniques for multi-omics integration.

To address this issue, we present TurboOmics (https://proteomics.cnic.es/TurboPutative/TurboOmicsApp.html), a web-based platform that facilitates the integration of proteomics and transcriptomics data with untargeted metabolomics. TurboOmics accepts quantitative data from these three omic fields along with metadata describing the experimental design. Metabolomics features uploaded to the platform are annotated based on their experimental mass/charge ratio using Ceu Mass Mediator, and the resulting annotations are simplified using TurboPutative. Additionally, TurboOmics provides basic functionalities for preprocessing quantitative data and sections for the exploratory data analysis. Multi-omics integration is performed using Multi-Omics Factor Analysis coupled with linear regressions between the projections and the metadata of the observations. This approach enables the fast and intuitive detection of biologically relevant factors and, consequently, the identification of highly correlated molecular entities (transcripts, proteins and metabolites) exhibiting differential behavior among sample groups. Furthermore, TurboOmics performs functional enrichment analysis of relevant proteins and transcripts using g:Profiler. Combined with the obtained putative annotations of the metabolomics features, this comprehensive analysis enables a deep and integral understanding of the biological system under investigation.

Reanalysis of the proteomics and metabolomics data from the Lorenzo et al. (2021) study with TurboOmics allows results to be obtained in significantly less time, providing more complete results and demonstrating protein-metabolite associations that were previously unappreciated. Hence, this platform will aid the transcriptomic, proteomic, and metabolomic communities in performing simple, fast, and intuitive integrative analyses with omics data, thereby gathering valuable information to discern associations between biomolecules and disease phenotypes and establish novel disease biomarkers.