Сравнение методов

	Анализ обогащения сигнальных путей	Протеомика
Область	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Год появления	2003–2005	1994–2003 (term coined 1994; shotgun proteomics established early 2000s)
Автор метода	Mootha et al. (2003); systematised by Subramanian et al. (2005)	Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)
Тип	Statistical functional annotation method	Quantitative omics pipeline
Основополагающий источник	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.	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.
Другие названия	PEA, overrepresentation analysis, ORA, functional enrichment analysis	proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics
Связанные	6	6
Сводка	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.	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.