TAMING THE BEAST

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Since tutorials are stored on GitHub, it is easy for anyone in the community to contribute to Taming the BEAST.

Press OK to reconstruct the past population dynamics ( Figure 11). Figure 11: Reconstructing the Bayesian Skyline plot in Tracer. It may be tempting to specify the maximum dimension for the model (each group contains only one coalescent event, thus N e N_e N e ​ changes at each branching time in the tree), making it as flexible as possible. This is the parameterization used by the Classic Skyline plot (Pybus et al., 2000), which is the direct ancestor of the Coalescent Bayesian Skyline plot. We also identified some issues with a few of the tutorials during the workshop and I’ll also be updating them soon as well. In the Partitions panel, import the nexus file with the alignment by navigating to File > Import Alignment in the menu and then finding the hcv.nexus file on your computer or simply drag and drop the file into the BEAUti window. Note that since BEAST 2.7 the filenames used here are the default filenames and should not need to be changed!)

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where the argument after N is the particleCount you specified in the XML, and xyz.log the trace log produced by the NS run. Why are some NS runs longer than others? So, the main parameters of the algorithm are the number of particles N and the subChainLength. N can be determined by starting with N=1 and from the information of that run a target standard deviation can be determined, which gives us a formula to determine N (as we will see later in the tutorial). The subChainLength determines how independent the replacement point is from the point that was saved, and is the only parameter that needs to be determined by trial and error – see FAQ for details.

We can leave the rest of the priors as they are and save the XML file. We want to shorten the chain length and decrease the sampling frequency so the analysis completes in a reasonable time and the output files stay small. (Keep in mind that it will be necessary to run a longer chain for parameters to mix properly). The sequences were all sampled in 1993 so we are dealing with a homochronous alignment and do not need to specify tip dates.The Coalescent Bayesian Skyline model uses the Kingman coalescent for each segment, which assumes that the sequences are a small sample drawn from a haploid population evolving under Wright-Fisher dynamics ( Figure 9). The model works by calculating the probability of observing the tree under this assumption. This essentially boils down to repeatedly asking the question of how likely it is for two lineages to coalesce (have a common ancestor) in a given time. Figure 9: The basic principle behind the coalescent. Figure from (Rosenberg & Nordborg, 2002). Estimates of N e N_e N e ​ therefore do not directly tell us something about the number of infected, nor the transmission rate. However, changes in N e N_e N e ​ can be informative about changes in the transmission rate or the number of infected (if they do not cancel out). Navigate to the Priors panel and select Coalescent Bayesian Skyline as the tree prior ( Figure 5). Figure 5: Choose the Coalescent Bayesian Skyline as a tree prior.

For the reconstruction of the population dynamics, we need two files, the *.log file and the *.trees file. The log file contains the information about the group sizes and population sizes of each segment, while the trees file is needed for the times of the coalescent events. It has already been more than two weeks since the second Taming the BEAST workshop took place on Waiheke island in New Zealand.

To change the number of segments we have to navigate to the Initialialization panel, which is by default not visible. Navigate to View > Show Initialization Panel to make it visible and navigate to it ( Figure 7). In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland. If there are any further issues, please raise them on the Github repository of the tutorial in question.

In practice, we can get away much smaller sub-chain lengths, which you can verify by running multiple NS analysis with increasing sub-chain lengths. If the ML and SD estimates do not substantially differ, you know the shorter sub-chain length was sufficient. How many particles do I need? The difference between the estimates is the way they are estimated from the nested sampling run. Since these are estimates that require random sampling, they differ from one estimate to another. When the standard deviation is small, the estimates will be very close, but when the standard deviations is quite large, the ML estimates can substantially differ. Regardless, any of the reported estimates are valid estimates, but make sure to report them with their standard deviation. How do I know the sub-chain length is large enough? However, the only informative events used by the Coalescent Bayesian Skyline plot are the coalescent events. Thus, using a maximally-flexible parameterization with only one informative event per segment often leads to erratic and noisy estimates of N e N_e N e ​ over time (especially if segments are very short, see Figure 6). Grouping segments together leads to smoother and more robust estimates.The analysis will take about 10 minutes to complete. Read through the next section while waiting for your results or start preparing the XML file for the birth-death skyline analysis. The Coalescent Bayesian Skyline parameterization The choice of the number of dimensions can also have a direct effect on how fast the MCMC converges ( Figure 14). The slower convergence with increasing dimension can be caused by e.g. less information per interval. To some extent it is simply caused by the need to estimate more parameters though. Figure 14: The ESS value of the posterior after running an MCMC chain with 1 0 7 10 Marginal likelihood: -12428.557546706481 sqrt(H/N)=(11.22272275528845)=?=SD=(11.252847709777592) Information: 125.94950604206919 During the workshop we realised that the materials we created and assembled for the workshop represent a very useful resource and that we could reach many more users by making them available to the community. The 2016 workshop organisers and participants.