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	ベイジアンChIP-seqピークコーリング	バリアントコーリング
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	2008–2009	2009–2010 (modern high-throughput era)
提唱者	Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
種類	Probabilistic signal detection pipeline	Computational genomics pipeline
原典	Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137.	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.
別名	Bayesian ChIP-seq analysis, probabilistic peak detection, Bayesian peak caller, ChIP-seq Bayesian enrichment calling	SNP calling, genotyping from sequencing, mutation detection, variant detection
関連	6	6
概要	Bayesian ChIP-seq peak calling applies probabilistic models — typically Poisson, negative binomial, or hidden Markov models with Bayesian inference — to detect genomic regions enriched for a protein of interest in chromatin immunoprecipitation followed by sequencing experiments. By explicitly modelling read-count noise and incorporating prior distributions, Bayesian callers yield posterior probabilities of enrichment rather than simple p-values, providing a principled framework for uncertainty quantification across the genome.	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.