Comparer des méthodes

	Analyse Protéomique	Alignement de séquences
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	1994–2003 (term coined 1994; shotgun proteomics established early 2000s)	1970 (global alignment); 1981 (local alignment)
Auteur d'origine	Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)	Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)
Type	Quantitative omics pipeline	Computational sequence analysis technique
Source fondatrice	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.	Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.
Alias	proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics	pairwise alignment, multiple sequence alignment, MSA, sequence comparison
Apparentées	6	6
Résumé	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.	Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation.