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	Anomenament de pics ChIP-seq bayesià	Estudi d'Associació Epigenòmica de Genoma Complet Bayesiana (Bayesian EWAS)
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	2008–2009	2010s (framework developed ~2013–2016)
Autor original	Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012	Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues
Tipus	Probabilistic signal detection pipeline	Statistical association analysis
Font seminal	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.	Richardson, S., Tsai, P. C., Bell, J. T., & Timpson, N. J. (2016). Bayesian approaches to studying associations between epigenetic marks and phenotypes. International Journal of Epidemiology, 45(3), 694–705.
Àlies	Bayesian ChIP-seq analysis, probabilistic peak detection, Bayesian peak caller, ChIP-seq Bayesian enrichment calling	Bayesian EWAS, B-EWAS, Bayesian methylation-wide association study, Bayesian epigenetic association analysis
Relacionats	6	4
Resum	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.	A Bayesian EWAS is a genome-scale association analysis that links epigenetic marks — most commonly CpG-site DNA methylation — to a phenotype or trait of interest, replacing or supplementing the classical frequentist p-value framework with a Bayesian probabilistic model. It yields posterior probabilities of association and credible intervals for each CpG site, allowing formal incorporation of prior biological knowledge and more principled handling of the multiple-testing burden intrinsic to testing hundreds of thousands of sites simultaneously.