방법 비교

	Phylogenetic Analysis	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	1960s-1981 (distance trees ~1967; ML framework formalised 1981)	2008–2010 (RNA-seq DE methodology established)
창시자	Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Computational inference method	Quantitative genomics pipeline
원전	Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates. ISBN: 978-0878931774	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.
별칭	molecular phylogenetics, phylogenetic inference, evolutionary tree reconstruction, phylogenomics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	5	6
요약	Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species.	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.