Linganisha mbinu

	Uchambuzi wa Proteomiki ya Mfululizo wa Muda	Uchambuzi wa Metabolomics
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)	1998–2002
Mwanzilishi	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)
Aina	Quantitative longitudinal omics pipeline	Quantitative omics pipeline
Chanzo asilia	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.
Majina mbadala	longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomics	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling
Zinazohusiana	6	6
Muhtasari	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.