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| ベイズ型バリアントコーリング× | RNA-seq 差次的発現× | |
|---|---|---|
| 分野 | バイオインフォマティクス | バイオインフォマティクス |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009 | 2008–2010 (RNA-seq DE methodology established) |
| 提唱者≠ | Mark DePristo, Eric Banks, and the Broad Institute GATK team | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| 種類≠ | Probabilistic genomic inference pipeline | Quantitative genomics pipeline |
| 原典≠ | 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. DOI ↗ | 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. DOI ↗ |
| 別名 | Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| 関連 | 6 | 6 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
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