Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз різноманітності мікробіому в часових рядах× | Аналіз диференційної експресії генів методом RNA-seq× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020) | 2008–2010 (RNA-seq DE methodology established) |
| Автор методу≠ | 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) |
| Тип≠ | Longitudinal observational / bioinformatics pipeline | Quantitative genomics pipeline |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | 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 |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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