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	Bayesiansk variantanropning – Probabilistisk SNP- och Indel-detektion	RNA-seq Differential Expression
Ämnesområde	Bioinformatik	Bioinformatik
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009	2008–2010 (RNA-seq DE methodology established)
Upphovsperson	Mark DePristo, Eric Banks, and the Broad Institute GATK team	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Typ	Probabilistic genomic inference pipeline	Quantitative genomics pipeline
Ursprungskälla	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.	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.
Alias	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Närliggande	6	6
Sammanfattning	Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based callers, Bayesian approaches explicitly model sequencing error, mapping uncertainty, and prior genotype frequencies to produce calibrated genotype likelihoods that can be used for downstream filtering and association testing.	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.