方法对比

	RNA-seq差异表达	序列比对
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2008–2010 (RNA-seq DE methodology established)	1970 (global alignment); 1981 (local alignment)
提出者	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)	Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)
类型	Quantitative genomics pipeline	Computational sequence analysis technique
开创性文献	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.	Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.
别名	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA	pairwise alignment, multiple sequence alignment, MSA, sequence comparison
相关	6	6
摘要	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.	Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation.