DIMet

DIMet performs differential analysis of stable-isotope resolved metabolomics (SIRM) data and integrates it with transcriptomics data to infer metabolic changes without complex flux analysis.

Key Features:

• Tracer Metabolomics Analysis: DIMet analyzes stable-isotope labeling patterns and metabolic abundances from SIRM experiments using isotopes such as 13C.
• Transcriptomics Integration: DIMet integrates metabolomics data with gene expression profiles for joint analysis.
• Metabologram Visualization: DIMet generates pathway-based "metabologram" visualizations that integrate metabolomics and transcriptomics data.
• Corrected Tracer Dataset Analysis: DIMet analyzes corrected tracer datasets across diverse systems, organisms, and isotopes.

Scientific Applications:

• Metabolic State Characterization: DIMet characterizes metabolic states in cells and organisms by tracing reaction rates across specific metabolic pathways using isotope-labeled substrates.
• Cancer Metabolism Research: DIMet was demonstrated through analysis of LDHA/B knockout glioblastoma cell lines compared to controls to study tumor metabolic symbiosis.
• Multi-omics Integration: DIMet provides deeper insights into cellular metabolic states through joint analysis of tracer metabolomics and transcriptomics datasets.

Methodology:

DIMet performs differential analysis of tracer metabolomics data and integrates it with transcriptomics data, using pathway-based approaches to infer metabolic changes without requiring complex flux analysis.