In this tutorial we will go through an analysis using bModelTest in BEAST v2.7.x and look into how to interpret the results. Practical: Selecting a substitution model It can also be used to investigate potential parameter correlations. It helps to quickly view median estimates and 95% highest posterior density intervals of the parameters, and calculates the effective sample sizes (ESS) of parameters. This program can be used for visual inspection and to assess convergence. Tracer ( ) is used to summarise the posterior estimates of the various parameters sampled by the Markov Chain. BEAUti2 is provided as a part of the BEAST2 package so you do not need to install it separately. The screenshots used in this tutorial are taken on a Mac OS X computer however, both programs will have the same layout and functionality on both Windows and Linux. For us it simply means that the interface will be the same on all platforms. BEAUti2 - Bayesian Evolutionary Analysis UtilityīEAUti2 is a graphical user interface tool for generating BEAST2 XML configuration files.īoth BEAST2 and BEAUti2 are Java programs, which means that the exact same code runs on all platforms. This tutorial is written for BEAST v2.7.x (Drummond & Bouckaert, 2014). Programs used in this Exercise BEAST2 - Bayesian Evolutionary Analysis Sampling TreesīEAST2 ( ) is a free software package for Bayesian evolutionary analysis of molecular sequences using MCMC and strictly oriented toward inference using rooted, time-measured phylogenetic trees. In addition, we can jump between models with empirical/estimated base frequencies, with/without gamma distributed rate heterogeneity and with/without invariant sites, resulting in a total of 203 x 2 x 2 x 2 = 1,624 possible model combinations. Ideally we would want to integrate over all possible substitution models, but since non-reversible models are mathematically inconvenient we restrict ourselves to the set of time-reversible (symmetric) nucleotide substitution models, which leaves us with 203 possible models. Note that bModelTest is only able to average over a subset of substitution models that are (a) implemented in BEAST2 and (b) that it knows how to move between. This can be interpreted as the posterior support of a model, which tells us how strongly the data and our prior beliefs support a model in comparison to other competing models. Thus, parameter estimates are effectively averaged over different substitution models, weighted by the support of each model.Ī useful consequence is that as we are exploring the space of different substitution models we also log the proportion of time that the Markov chain spends in a particular model state. This allows us to treat the substitution model as a nuisance parameter and integrate over all available (more on this later) substitution models while simultaneously estimating the phylogeny and other model parameters. bModelTest uses reversible jump MCMC (rjMCMC), which allows the Markov chain to jump between states representing different possible substitution models, much like we jump between different parameter states in standard Bayesian MCMC inference. In this tutorial, we will use BEAST2’s model averaging tool bModelTest (Bouckaert & Drummond, 2017) to select the most appropriate substitution model for the primate mitochondrial data set we already saw in the introductory tutorial. For this reason, researchers have often based their model choice on common conventions rather than on which model is most appropriate for their data.įortunately, nowadays we can be more sophisticated in our modeling choices and let the data inform us about which model is most appropriate using Bayesian model averaging. All of these choices leads to a bewildering number of different models to choose from. & r_ π X in the equation above) or fix them at their empirical frequencies. Q = ( − r a c π C r a g π G r a t π T r a c π A − r c g π G r c t π T r a g π A r c g π C − r g t π T r a t π A r c t π C r g t π G − ) = ( − r a c r a g r a t r a c − r c g r c t r a g r c g − r g t r a t r c t r g t − ) × ( π A 0 0 0 0 π C 0 0 0 0 π G 0 0 0 0 π T ) \displaystyle Q =
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |