Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Tijdreeksanalyse van Microbiële Diversiteit× | RNA-seq Differentiele Expressie× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020) | 2008–2010 (RNA-seq DE methodology established) |
| Grondlegger≠ | Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Type≠ | Longitudinal observational / bioinformatics pipeline | Quantitative genomics pipeline |
| Oorspronkelijke bron≠ | Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. DOI ↗ | 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 ↗ |
| Aliassen | longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Verwant≠ | 5 | 6 |
| Samenvatting≠ | Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome. | 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. |
| ScholarGateGegevensset ↗ |
|
|