Methoden vergleichen

	Zeitreihen-Proteomik-Analyse	Metabolomics Analysis
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)	1998–2002
Urheber	Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows	Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)
Typ	Quantitative longitudinal omics pipeline	Quantitative omics pipeline
Wegweisende Quelle	Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8.	Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.
Aliasnamen	longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomics	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling
Verwandt	6	6
Zusammenfassung	Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for temporal data, the method identifies proteins with significant expression trends, oscillatory patterns, or delayed responses that cannot be detected in single time-point studies.	Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time.