השוואת שיטות

	ניתוח מטבולומי רב-אומי (Multi-omics Metabolomics Analysis)	ניתוח פרוטאומי
תחום	ביואינפורמטיקה	ביואינפורמטיקה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)	1994–2003 (term coined 1994; shotgun proteomics established early 2000s)
הוגה השיטה	Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)	Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)
סוג	Integrative computational pipeline	Quantitative omics pipeline
מקור מכונן	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.	Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50.
כינויים	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics	proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics
קשורות	5	6
תקציר	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.	Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology.