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| Bayesian Metabolomics Analysis× | RNA-seq Differential Expression× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2005–2010 | 2008–2010 (RNA-seq DE methodology established) |
| Urheber≠ | Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Typ≠ | Probabilistic statistical pipeline | Quantitative genomics pipeline |
| Wegweisende Quelle≠ | Rogers, S., Scheltema, R. A., & Girolami, M. A. (2009). Bayesian analysis of metabolomic NMR data. Bioinformatics, 25(14), 1809-1815. 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 ↗ |
| Aliasnamen | Bayesian metabolomics, probabilistic metabolomics, Bayesian metabolite profiling, Bayesian metabolic flux analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributions and propagating uncertainty throughout the analysis, it yields more calibrated probability statements about metabolic differences than classical frequentist testing alone. | 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. |
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