Dual multiple change-point model leads to more accurate recombination detection
Author(s) -
Vladimir N. Minin,
Karin S. Dorman,
Fang Fang,
Marc A. Suchard
Publication year - 2005
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bti459
Subject(s) - markov chain monte carlo , recombination , monte carlo method , algorithm , posterior probability , reversible jump markov chain monte carlo , computer science , bayesian probability , biological system , statistical physics , mathematics , statistics , biology , physics , artificial intelligence , genetics , gene
We introduce a dual multiple change-point (MCP) model for recombination detection among aligned nucleotide sequences. The dual MCP model is an extension of the model introduced previously by Suchard and co-workers. In the original single MCP model, one change-point process is used to model spatial phylogenetic variation. Here, we show that using two change-point processes, one for spatial variation of tree topologies and the other for spatial variation of substitution process parameters, increases recombination detection accuracy. Statistical analysis is done in a Bayesian framework using reversible jump Markov chain Monte Carlo sampling to approximate the joint posterior distribution of all model parameters.
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