TAMING THE BEAST

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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. The sequences were all sampled in 1993 so we are dealing with a homochronous alignment and do not need to specify tip dates. 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). 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).

An NS analysis produces two trace log files: one for the nested sampling run (say myFile.log) and one with the posterior sample ( myFile.posterior.log). NS works in theory if and only if the points generated at each iteration are independent. If you already did an MCMC run and know the effective sample size (ESS) for each parameter, to be sure every parameter in every sample is independent you can take the length of the MCMC run divided by the smallest ESS as sub-chain length. This tend to result in quite large sub-chain lengths. The Coalescent Bayesian Skyline divides the time between the present and the root of the tree (the tMRCA) into segments, and estimates a different effective population size ( N e N_e N e ​ ) for each segment. The endpoints of segments are tied to the branching times (also called coalescent events) in the tree ( Figure 6), and the size of segments is measured in the number of coalescent events included in each segment. The Coalescent Bayesian Skyline groups coalescent events into segments and jointly estimates the N e N_e N e ​ ( bPopSizes parameter in BEAST) and the size of each segment ( bGroupSizes parameter). To set the number of segments we have to change the dimension of bPopSizes and bGroupSizes (note that the dimension of both parameters always has to be the same). Note that the length of a segment is not fixed, but dependent on the timing of coalescent events in the tree ( Figure 6), as well as the number of events contained within a segment ( bGroupSizes). Figure 6: Example tree where the red dotted lines show the time-points of coalescent events. 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? SCOTTI Tutorial: NEW Reconstruct transmission trees using within-host data with an approximate structured coalescent.

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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. 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.

Once the analyses have run, open the log file in Tracer and compare estimates and see whether the analyses substantially differ. You can also compare the trees in DensiTree. 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 10It has already been more than two weeks since the second Taming the BEAST workshop took place on Waiheke island in New Zealand.