Selected article for: "bayesian likelihood and phylogenetic inference"
Author: Chan, Joseph M.; Rabadan, Raul
Title: Quantifying Pathogen Surveillance Using Temporal Genomic Data
  • Document date: 2013_1_29
    • ID: u2t1x89m_24
    Snippet: While these phylogenetic techniques can examine the fit of a number of evolutionary models, they suffer from problems of robustness. For example, tree topology can be highly unstable; the addition or deletion of a single sequence can radically restructure the tree. Moreover, different methods of phylogenetic inference, such as maximum likelihood or Bayesian inference, can lead to variable results, rendering interpretation of surveillance complica.....
    Document: While these phylogenetic techniques can examine the fit of a number of evolutionary models, they suffer from problems of robustness. For example, tree topology can be highly unstable; the addition or deletion of a single sequence can radically restructure the tree. Moreover, different methods of phylogenetic inference, such as maximum likelihood or Bayesian inference, can lead to variable results, rendering interpretation of surveillance complicated. Finally, computation time, particularly for BEAST, can be very expensive; for data sets of more than 1,000 sequences, several weeks may be needed for the MCMC to converge to a stable tree solution. In our analysis of 158 human H5N1 HA sequences, p2 coefficients needed days of computation to complete, whereas q2 coefficient analysis was finished in a matter of seconds.

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