Compară metode

	Analiza proteomică multi-omică	Expresia Diferențială RNA-seq
Domeniu	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline
Anul apariției	2010s (integrative multi-omics frameworks emerged ~2012–2019)	2008–2010 (RNA-seq DE methodology established)
Autorul original	Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Tip	Integrative computational pipeline	Quantitative genomics pipeline
Sursa seminală	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.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Denumiri alternative	integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Înrudite	6	6
Rezumat	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.