Compara mètodes

	Anàlisi Filogenètica de Sèries Temporals	Expressió Diferencial en RNA-seq
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	2000s (molecular clock methods earlier; BEAST framework 2007)	2008–2010 (RNA-seq DE methodology established)
Autor original	Alexei J. Drummond, Andrew Rambaut, and colleagues	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Tipus	Evolutionary bioinformatics pipeline	Quantitative genomics pipeline
Font seminal	Drummond, A. J., & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7, 214.	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.
Àlies	temporal phylogenetics, time-resolved phylogenetics, molecular clock phylogenetics, phylodynamics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Relacionats	6	6
Resum	Time-series phylogenetic analysis reconstructs the evolutionary history of organisms or genetic variants using sequences sampled at known time points. By incorporating sampling dates directly into the model, it estimates divergence times, substitution rates, and ancestral relationships on an absolute timescale — making it essential for studying viral outbreaks, ancient DNA dynamics, and rapid microbial evolution.	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.