Võrdle meetodeid

	RNA-seq diferentsiaalne ekspressioon	Variant Calling
Valdkond	Bioinformaatika	Bioinformaatika
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	2008–2010 (RNA-seq DE methodology established)	2009–2010 (modern high-throughput era)
Looja	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Tüüp	Quantitative genomics pipeline	Computational genomics pipeline
Algallikas	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.	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.
Rööpnimetused	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA	SNP calling, genotyping from sequencing, mutation detection, variant detection
Seotud	6	6
Kokkuvõte	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.	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.