Krahasoni metodat

	Analiza e shprehjes diferenciale të RNA-seq	Identifikimi i Varianteve
Fusha	Bioinformatikë	Bioinformatikë
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2008–2010 (RNA-seq DE methodology established)	2009–2010 (modern high-throughput era)
Krijuesi	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)
Lloji	Quantitative genomics pipeline	Computational genomics pipeline
Burimi themelues	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.
Emërtime të tjera	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA	SNP calling, genotyping from sequencing, mutation detection, variant detection
Të lidhura	6	6
Përmbledhja	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.