Selected article for: "Bayesian Markov chain and common ancestor"
Author: Wang, Yanqun; Liu, Di; Shi, Weifeng; Lu, Roujian; Wang, Wenling; Zhao, Yanjie; Deng, Yao; Zhou, Weimin; Ren, Hongguang; Wu, Jun; Wang, Yu; Wu, Guizhen; Gao, George F.; Tan, Wenjie
Title: Origin and Possible Genetic Recombination of the Middle East Respiratory Syndrome Coronavirus from the First Imported Case in China: Phylogenetics and Coalescence Analysis
  • Document date: 2015_9_8
    • ID: x6sjdglm_12
    Snippet: Phylogenetic analysis. We downloaded all (n Ï­ 92) available fulllength genome sequences of MERS-CoV from GenBank and used RAxML (20) for phylogenetic analyses of the complete genome, the ORF1ab gene, and the S gene, respectively. One thousand bootstrap replicates were run. Furthermore, the Bayesian Markov chain Monte Carlo method, implemented in BEAST (21) , was used to estimate the time to the most recent common ancestor. Twelve different model.....
    Document: Phylogenetic analysis. We downloaded all (n Ï­ 92) available fulllength genome sequences of MERS-CoV from GenBank and used RAxML (20) for phylogenetic analyses of the complete genome, the ORF1ab gene, and the S gene, respectively. One thousand bootstrap replicates were run. Furthermore, the Bayesian Markov chain Monte Carlo method, implemented in BEAST (21) , was used to estimate the time to the most recent common ancestor. Twelve different model combinations were applied. For all the analyses, we used the general time-reversible nucleotide substitution model with gamma-distributed rate heterogeneity. Bayesian Markov chain Monte Carlo analysis was run for 50 million steps. Trees and parameters were sampled every 5,000 steps, with the first 10% removed as burn-in.

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