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| Analisis Proteomik× | Analisis Ungkapan Perbezaan RNA-seq× | |
|---|---|---|
| Bidang | Bioinformatik | Bioinformatik |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1994–2003 (term coined 1994; shotgun proteomics established early 2000s) | 2008–2010 (RNA-seq DE methodology established) |
| Pengasas≠ | Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Jenis≠ | Quantitative omics pipeline | Quantitative genomics pipeline |
| Sumber perintis≠ | 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. link ↗ | 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. DOI ↗ |
| Alias | proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
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