방법 비교

	변이 호출	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2009–2010 (modern high-throughput era)	2008–2010 (RNA-seq DE methodology established)
창시자	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Computational genomics pipeline	Quantitative genomics pipeline
원전	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.	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	SNP calling, genotyping from sequencing, mutation detection, variant detection	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	6	6
요약	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.	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.