Compară metode

	Analiza Metabolomică Diferențială	Analiza Multi-Omics a Metabolomului
Domeniu	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s–2010s (field formalised alongside mass spectrometry advances)	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)
Autorul original	Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations	Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)
Tip	Quantitative comparative omics pipeline	Integrative computational pipeline
Sursa seminală	Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0 — making metabolomics more meaningful. Nucleic Acids Research, 43(W1), W251–W257.	Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.
Denumiri alternative	comparative metabolomics, differential metabolite profiling, metabolomic differential analysis, DMA	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics
Înrudite	6	5
Rezumat	Differential metabolomics analysis is a computational pipeline that identifies metabolites whose abundance levels differ significantly between two or more biological conditions — such as disease versus control, treated versus untreated, or different developmental stages. By integrating mass spectrometry or NMR data with statistical modelling and pathway databases, it translates raw spectral measurements into biologically interpretable lists of perturbed metabolic features and the pathways they implicate.	Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone.