Linganisha mbinu

	Upigaji Tofauti wa Vigezo	Uchambuzi wa Utekelezaji Tofauti wa RNA-seq
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2009–2013 (field matured with NGS; seminal tools 2009–2013)	2008–2010 (RNA-seq DE methodology established)
Mwanzilishi	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)
Aina	Comparative genomic analysis pipeline	Quantitative genomics pipeline
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	2	6
Muhtasari	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.