Metabolomics and Multi-Omics

The IFX Core is actively working on developing omic and multi-omic algorithms and tools to help interpret these data. These efforts are highly collaborative and involve investigators within NCATS’ DPI and beyond.

• Development of multi-omic (e.g. metabolomics, proteomics, transcriptomic), pathway-based and numerical-based integration methods
• Development of methods for the analysis of dose response transcriptomic profiles
• Recent advances in mass spectrometry-based computational metabolomics
• Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

Relational Database of Metabolic Pathways

Code: https://github.com/ncats/RaMP-DB

Interface: https://rampdb.nih.gov/

The Relational Database of Metabolic Pathways (RaMP-DB) is a publicly available relational database that integrates multiple sources of biological, chemical and analyte (metabolite, protein, gene) annotations. The source code for building the database is available, and a user-friendly application to query the database is provided. RaMP-DB also supports pathway enrichment analysis for multi-omics data input.

COMETS Analytics

https://github.com/CBIIT/R-cometsAnalytics    
https://www.comets-analytics.org/

COMETS Analytics supports and streamlines consortium-based analyses of metabolomics data. Unique features of COMETS Analytics include an algorithmic and reproducible approach to diagnose, document and fix model issues. These features enable users to run standardized models across many cohorts in a timely manner and eliminate the need for manually customizing models by cohort, which can be very time consuming and error prone.

Metabolomics and Multi-Omics Profiling to Identify Putative Biomarkers and Elucidate Disease Processes

• Evaluate metabolomic and proteomic profiles in 2-D and 3-D lung models to understand cellular responses to infection.
• Conduct metabolomic analysis of human blood samples in prospective studies to identify markers of disease severity (e.g., macular degeneration, COVID-19 and other infectious diseases).
• Characterize the effects of diet and prebiotic supplementation on the microbiome and metabolome that lead to the development of aberrant crypt foci and behavioral changes, respectively.
• Use comprehensive metabolomic and lipidomic characterization of dedifferentiated liposarcoma cell lines to identify MDM2-dependent molecular rewiring that underlies chemoresistance.