So sánh phương pháp

	Phân tích phát sinh loài Bayes	Nghiên cứu liên kết bộ gen trên toàn bộ bộ gen theo phương pháp Bayes	Căn chỉnh trình tự
Lĩnh vực	Tin sinh học	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	1996–2001	2007–2009 (formal statistical framework)	1970 (global alignment); 1981 (local alignment)
Người khởi xướng	Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)	Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)	Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)
Loại	Probabilistic inference method	Statistical genetic association analysis	Computational sequence analysis technique
Công trình gốc	Ronquist, F., & Huelsenbeck, J. P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12), 1572–1574.	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.	Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.
Tên gọi khác	Bayesian phylogenetics, Bayesian inference of phylogeny, MCMC phylogenetics, Bayesian molecular phylogenetics	Bayesian GWAS, Bayesian genome-wide association analysis, Bayesian GWA study, BF-GWAS	pairwise alignment, multiple sequence alignment, MSA, sequence comparison
Liên quan	3	5	6
Tóm tắt	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.	Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a continuous scale, and supports principled fine-mapping of causal variants within associated loci. It is widely used in complex trait genetics, population genomics, and translational research where uncertainty quantification and multi-variant modeling matter.	Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation.