TAMING THE BEAST

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Choosing the dimension for the Bayesian Skyline can be rather arbitrary. If the dimension is chosen too low, not all population size changes are captured, but if it is chosen too large, there may be too little information in a segment to support a robust estimate. When trying to decide if the dimension is appropriate it may be useful to consider the average number of informative (coalescent) events per segment. (A tree of n n n taxa has n − 1 n-1 n − 1 coalescences, thus N e N_e N e ​ in each segment is estimated from on average n − 1 d \frac{n-1}{d} d n − 1 ​ informative data points). Would this number of random samples drawn from a hypothetical distribution allow you to accurately estimate the distribution? If not, consider decreasing the dimension. Navigate to Analysis > Bayesian Skyline Reconstruction. From there open the *.trees file. To get the correct dates in the analysis we should specify the Age of the youngest tip. In our case it is 1993, the year where all the samples were taken. If the sequences were sampled at different times (heterochronous data), the age of the youngest tip is the time when the most recent sample was collected.

We hope that the community will play an active role in curating the tutorials, either by updating or correcting existing tutorials, or by contributing new tutorials. 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. The idea of holding a BEAST 2 workshop has been brewing for a while, motivated by the need for a Bayesian phylogenetics workshop that is focused on BEAST 2 and facilitates exchanges between developers and (both current and future) BEAST 2 users. 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 In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland.Note that since BEAST 2.7 the filenames used here are the default filenames and should not need to be changed!) 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). Because we shortened the chain most parameters have very low ESS values. If you like, you can compare your results with the example results we obtained with identical settings and a chain of 30,000,000 ( hcv_coal_30M.log). 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?

The workshop organisers and participants outside of the London School of Hygiene and Tropical Medicine. 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. This sets the number of segments equal to 4 (the parameter dimension), which means N e N_e N e ​ will be allowed to change 3 times between the tMRCA and the present (if we have d d d segments, N e N_e N e ​ is allowed to change d − 1 d-1 d − 1 times).

Get to know the advantages and disadvantages of the Coalescent Bayesian Skyline Plot and the Birth-Death Skyline.