Frequently Asked Questions

BEAST is a cross-platform program for Bayesian analysis of molecular sequences using MCMC. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program, called BEAUti, for setting up standard analyses and a suit of programs for analysing the results.

We have renamed the release that would have been called BEAST v1.10.5 as BEAST X v10.5.0. This is to distinguish it from the independent project called BEAST 2 – see: https://www.beast2.org. These projects have been running in parallel for many years which has restricted this BEAST project to using the v1 major release version and only incrementing the minor release version. We have renamed this project BEAST X to denote its orthogonal axis in project-space and thus call this v10.#.# as the new major version. The next major version of BEAST X will be v11.0.0, and so on.

Look at this page for instructions about installing BEAST on different operating systems

Accurate model comparison in Bayesian phylogenetics is typically performed using path sampling, stepping-stone sampling or generalized stepping-stone sampling.

See this page for an explanation of various approaches to summarizing trees.

The tree produced by TreeAnnotator (denoted the maximum clade credibility or MCC tree) is not a consensus tree such as that produced by the 'sumt' command in MrBayes. Instead, TreeAnnotator picks one of the trees actually present in the sample produced by BEAST - thus it is a tree that was actually visited by the MCMC sampler. The tree it picks is the one that has the highest product of all the clade credibilities (posterior probabilities) for all the clades in the tree. The motivation with this is to find a single 'point estimate' tree that is in some way central to the distribution of trees. This tree is then given (annotated with) summary information for the full set of trees from the sample. For example, it is given average node heights, credible intervals for the node heights, rates, etc.

See this page for an explanation of various approaches to summarizing trees.

The Effective Sample Size (ESS) of a parameter sampled from an MCMC (such as BEAST) is the number of effectively independent draws from the posterior distribution that the Markov chain is equivalent to.

In BEAUti, on the MCMC tab, click the checkbox 'Sample from prior only - create empty alignment', save the XML and run with BEAST. Alternatively, in the XML file, remove (or comments out) the entries in the <likelihood> block.

The simple answer is that you may not want to - BEAST will sample the root position along with the rest of the nodes in the tree. If you then calculate the proportion of trees that have a particular root, you obtain a posterior probability for this root position. However if you have a strong prior for an outgroup then you can constrain the ingroup to be monophyletic.

A description of the coalescent tree priors (or demographic priors) can be found here. There are essentially three different non-parametric demographic priors available: the Bayesian Skyline, the (GMRF) Bayesian Skyride and the Bayesian Skygrid. Basically, we suggest using Skyride over Skyline as it is a straightforward development of the Skyline with fewer user specified options.

The question of Skygrid vs Skyride is more complex. Skyride, like the original Skyline, scales its demographic curve to the height/age of the tree, with the changes in population sizes being concordant with the nodes in the tree. That is, as the tree grows and shrinks over the course of the MCMC, the Skyride timescale does too. Skygrid on the other hand requires you to define a fixed time line and grid points where the population size changes. The tree scales within this grid (i.e., the grid doesn't change even though the tree does). This is useful when you know what the timeline of the process is. Skygrid allow allows multi-locus analysis but this is probably not useful for viruses (unless they are independent epidemics being controlled by the same process). Finally, Skygrid allows covariates of the population dynamics to be incorporated and tested.

Yes. BEAST will evaluate each component in order starting with the priors. If any of these are zero, then the rest of the posterior is not calculated. Thus it is particularly important that constraints, like <booleanLikelihood> and <uniformPrior> which may give zero probabilities, are put at the beginning of the <prior> section:

<mcmc id="mcmc" chainLength="1000000" autoOptimize="true">
    <posterior id="posterior">
        <prior id="prior">
            <uniformPrior id="constraint" lower="90.0" upper="100.0">
                <parameter idref="rootHeight"/>
            </uniformPrior>
            <coalescentLikelihood idref="coalescent"/>
        </prior>
        <likelihood id="likelihood">
            <treeLikelihood idref="treeLikelihood"/>
        </likelihood>
    </posterior>

Yes, you can insert a starting tree in Newick format into the BEAST XML using a text editor:

<newick id="startingTree">
    insert you starting tree here in Newick format
</newick>

The <treeModel> XML element then needs to contain a reference to this starting tree XML element:

<newick idref="startingTree"/>

Alternatively, you can provide a starting tree using BEAUti, in the Trees panel. You can load a file containg one or more trees (in Newick format) into BEAUti using the "Import Data" menu option.

In short, you need to remove all the operators that act on the treeModel. In BEAUti, you can do this by deselecting the following operators: narrow exchange, wide exchange, Wilson Balding and subtree slide. Alternatively, you can remove these operators from the XML using a text editor (but don't remove the operators acting upon treeModel.rootHeight or treeModel.internalNodeHeights). Without these operators, the actual topology of the tree will not be altered.

If you want to print the initial tree for your analysis printed to screen, you can add the following to the BEAST XML file using a text editor:

<report>
    <treeModel idref="treeModel"/>
</report>