Vertaile menetelmiä

	Differentiaalinen varianttien kutsuminen	RNA-seq-differentiaaliekspressioanalyysi
Tieteenala	Bioinformatiikka	Bioinformatiikka
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2009–2013 (field matured with NGS; seminal tools 2009–2013)	2008–2010 (RNA-seq DE methodology established)
Kehittäjä	Multiple groups; key tools: VarScan (Koboldt et al.), MuTect (Cibulskis et al.), GATK Haplotype Caller (DePristo et al.)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Tyyppi	Comparative genomic analysis pipeline	Quantitative genomics pipeline
Alkuperäislähde	Koboldt, D.C., Zhang, Q., Larson, D.E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., & Wilson, R.K. (2012). VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3), 568–576.	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.
Rinnakkaisnimet	somatic variant calling, comparative variant analysis, tumor-normal variant calling, differential SNV/indel calling	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Liittyvät	2	6
Tiivistelmä	Differential variant calling is a bioinformatics pipeline that identifies genetic variants — single nucleotide variants (SNVs), small insertions/deletions (indels), and structural variants — that are present in one biological sample or condition but absent (or significantly enriched) in a paired reference sample. The canonical application is tumor-versus-normal cancer genomics, where somatic mutations unique to the tumor are distinguished from germline variants shared with normal tissue. The same logic applies to comparing treated vs. untreated cell lines, evolved vs. ancestral strains, or case vs. control cohorts in population genomics.	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.