Linganisha mbinu

	Uchambuzi wa proteomiki ya omiki-nyingi	Uchambuzi wa Metabolomics
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2010s (integrative multi-omics frameworks emerged ~2012–2019)	1998–2002
Mwanzilishi	Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work	Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)
Aina	Integrative computational pipeline	Quantitative omics pipeline
Chanzo asilia	Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.	Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.
Majina mbadala	integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling
Zinazohusiana	6	6
Muhtasari	Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss.	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.