Сравнение методов

	Байесовский анализ разнообразия микробиома	Байесовский филогенетический анализ
Область	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Год появления	2010s (Dirichlet-Multinomial approach formalized ~2012; extensions ongoing)	1996–2001
Автор метода	Ian Holmes, Katie Harris, Christopher Quince (Dirichlet-Multinomial Mixture framework, 2012); broader Bayesian microbiome modeling community	Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)
Тип	Probabilistic/Bayesian pipeline for compositional count data	Probabilistic inference method
Основополагающий источник	Holmes, I., Harris, K., & Quince, C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS ONE, 7(2), e30126.	Ronquist, F., & Huelsenbeck, J. P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12), 1572–1574.
Другие названия	Bayesian microbiome profiling, Dirichlet-Multinomial microbiome analysis, Bayesian alpha/beta diversity, probabilistic microbiome diversity	Bayesian phylogenetics, Bayesian inference of phylogeny, MCMC phylogenetics, Bayesian molecular phylogenetics
Связанные	5	3
Сводка	Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while propagating uncertainty through the entire inference chain. Unlike frequentist rarefaction-based approaches, Bayesian methods treat taxon counts as draws from a latent composition, enabling credible intervals on diversity metrics and principled comparison across groups with unequal sequencing depth.	Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference yields a credible set of trees with associated posterior probabilities, providing a principled measure of phylogenetic uncertainty. It is the dominant framework for estimating divergence times and ancestral relationships in molecular evolution.