Sammenlign metoder

	Differensiell metabolomikkanalyse	Differensiell proteomikk-analyse
Fagfelt	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2000s–2010s (field formalised alongside mass spectrometry advances)	Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)
Opphavsperson	Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations	Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)
Type	Quantitative comparative omics pipeline	Quantitative omics pipeline
Opprinnelig kilde	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.	Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1(5), 376–386.
Alias	comparative metabolomics, differential metabolite profiling, metabolomic differential analysis, DMA	comparative proteomics, quantitative differential proteomics, differential protein expression analysis, DPA
Relaterte	6	1
Sammendrag	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.	Differential proteomics analysis is a quantitative pipeline that identifies proteins whose abundance levels change significantly between two or more biological conditions — such as healthy versus diseased tissue, treated versus untreated cells, or different developmental stages. By combining mass spectrometry-based detection with statistical testing, the method generates ranked lists of differentially expressed proteins that can be linked to biological pathways, disease mechanisms, or drug targets.