Salīdzināt metodes
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| Beiziešu proteomikas analīze× | RNA-seq diferenciālās ekspresijas× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s (major developments 2003–2010) | 2008–2010 (RNA-seq DE methodology established) |
| Autors≠ | Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Tips≠ | Probabilistic inference pipeline | Quantitative genomics pipeline |
| Pirmavots≠ | Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925. 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 ↗ |
| Citi nosaukumi | Bayesian protein quantification, Bayesian peptide inference, probabilistic proteomics, Bayesian mass spectrometry analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives. | 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. |
| ScholarGateDatu kopa ↗ |
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