Methoden vergelijken

	Tijdreeks-variantdetectie	RNA-seq Differentiele Expressie
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2009–2012	2008–2010 (RNA-seq DE methodology established)
Grondlegger	Pioneered in cancer genomics by Nik-Zainal, Campbell, and collaborators (Sanger Institute/Wellcome Trust)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Type	Longitudinal genomic analysis pipeline	Quantitative genomics pipeline
Oorspronkelijke bron	Nik-Zainal, S., et al. (2012). The life history of 21 breast cancers. Cell, 149(5), 994–1007.	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.
Aliassen	longitudinal variant calling, temporal somatic mutation detection, serial variant calling, time-course variant detection	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Verwant	1	6
Samenvatting	Time-series variant calling is a bioinformatics pipeline that identifies and tracks genomic variants — typically somatic mutations — across multiple sequencing samples collected from the same subject at different time points. It is most widely applied in cancer genomics to reconstruct tumour evolution, monitor minimal residual disease, and detect the emergence of therapy-resistant clones. By jointly modelling variant allele frequencies across the temporal dimension, the method distinguishes true somatic changes from sequencing noise and estimates clonal dynamics over time.	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.