Time-stamped data

This tutorial estimates the rate of evolution from a set of virus sequences isolated at different points in time (heterochronous or time-stamped data). The data consist of 129 sequences from the G (attachment protein) gene of human respiratory syncytial virus subgroup A (RSVA). These viral sequences come from various parts of the world at isolation dates ranging from 1956 to 2002 ((Zlateva et al., 2004), (Zlateva et al., 2005)). RSVA infects the lower respiratory tract and causes symptoms that are often indistinguishable from the common cold. Nearly all children are infected by age 3, and a small percentage (<3%) develops a more serious inflammation of the bronchioles requiring hospitalisation.

The aim of this tutorial is to estimate:

  • the rate of molecular evolution

  • the date of the most recent common ancestor

  • the phylogenetic relationships with measures of statistical support.

The following software will be used in this tutorial:

  • Java 1.8 - either Oracle Java or OpenJDK. If you are using a more recent version of Java than 1.8, it may affect BEAUti and some GUIs. In this case, we recommend you to download BEAST with JRE.

  • BEAST - this package contains the BEAST program, BEAUti, DensiTree, TreeAnnotator and other utility programs. This tutorial is written for BEAST v2.6.x, which has support for multiple partitions. It is available for download from http://www.beast2.org.

  • Tracer - this program is used to explore the output of BEAST (and other Bayesian MCMC programs). It graphically and quantitively summarises the distributions of continuous parameters and provides diagnostic information. At the time of writing, the current version is v1.7.2. It is available for download from http://beast.community/tracer.

  • FigTree - this is an application for displaying and printing molecular phylogenies, in particular those obtained using BEAST. At the time of writing, the current version is v1.4.4. It is available for download from http://beast.community/figtree.

  • Beagle (optional) - this is a high-performance library that can perform the core calculations at the heart of
    most Bayesian and maximum likelihood phylogenetics packages. https://github.com/beagle-dev/beagle-lib/wiki.


The NEXUS alignment

The data is in a file called RSV2.nex. This file contains an alignment of 129 sequences from the G gene of RSVA virus. These sequences are 629 nucleotides long. Click the link above and the raw data will be displayed on your web browser. You can then right-click, “Save Page As”, and save the data as RSV2.nex in your working directory.

Alternatively, you can find this data in the examples/nexus directory in the directory where BEAST was installed. If you prefer to use BEAUti, you can select the menu File => Set working dir => BEAST. This will make it easier to find example and tutorial files distributed with BEAST, precluding directory navigation.

Import and split the alignment

There are two options to import this alignment into BEAUti.

  1. From the menu, to click Import Alignment.

  2. Drag and drop the file into the Partitions panel. If there is a pop-up dialog to ask you what to add, then you need to select Import Alignment from the drop-down list as shown in Figure 1.

Add partition
Figure 1: Add the alignment from drag-and-drop

The gene we have at hand codes for a protein, meaning its sequence can be seen as a series of triplets, or codons, with each codon being later translated into an amino acid (remember the central dogma of molecular biology!). A string of aminoacids then constitutes a protein.

For our purposes, what matters is that we have now known for a while that different positions in each codon evolve at different rates, which has to do with what we call the “genetic code” (look up “genetic code redundancy” online). In knowing this, we can incorporate this difference in evolutionary rates by splitting the alignment into three partitions, each representing one of the three codon positions.

To do this we will click the Split button at the bottom of the Partitions panel and then select the 1 + 2 + 3 frame 3 from the drop-down menu (Figure 2).

split alignment
Figure 2: Split the alignment

This specifies that the first full codon starts at the third nucleotide in the alignment (it just so happens that our gene sequence starts at the third position in the sequence), and creates three rows in the partitions panel.

Linking or unlinking models

For the reasons mentioned above, we want to allow each partition to have its own substitution model. This will make it possible for each codon position to have a different relative evolutionary rates. But we want to still have all codon positions (i) evolving along the same phylogenetic tree, and (ii) keeping their evolutionary rates proportionally the same along that tree.

In order to achieve this, you will have to link (i) the tree and (ii) clock models (these deal with how rates vary along the tree) across the three partitions, and name them tree and clock, respectively. This requires selecting all three partitions first, and clicking the buttons Link Clock Models and Link Trees. The partition panel should now look something like this:

Figure 3: A screenshot of the Partitions tab in BEAUti

Tip dates

By default all the taxa are assumed to have a date of zero (i.e., the sequences are assumed to be sampled at the same time). In our case, the RSVA sequences have been sampled at various dates going back to the 1950s. The actual year of sampling is given in the name of each taxon and we could simply edit the value in the Date column of the table to reflect these.

Knowing the absolute times of sampling and having samples taken over time – rather than all samples having been collected only at the present moment – is crucial because otherwise we can’t tell the rate r and time t apart, where both contribute to the accumulated evolutionary change, D = r * t. We could obtain any value of D as the result of (1/2r * 2t) or (2r * 1/2t), etc. Can you see how r and t will be conflated? We thus need to have one or more samples taken at known absolute t values to be able to disentangle r and t. This is called “calibrating” the tree.

Back to our data, when taxa names contain the calibration information, as is our case, then a convenient way to specify the dates of the sequences in to let BEAUti figure it out. You can do that by clicking the checkbox Use tip dates, and then clicking the Auto-configure button at the top right of the Tip Dates panel. This will make a dialog box appear.

Figure 4: Guess dates dialog

Select the option to use everything, choose after last from the drop-down box and type s into the corresponding text box. This will extract the trailing numbers from the taxon names after the last lower-case letter s, which are interpreted as the year (since 1900 in our case) that the sample was isolated. You may have noticed these dates are specified as Since some time in the past: this is known as forward time in a phylodynamic analysis.

The dates panel should now look something like this:

Figure 5: Dates panel

Thanks to calibration, the node height of the sampled phylogenetic tree will be scaled to some unit of absolute time (here the unit is year), which makes our phylogenetic trees be what we call “time trees”. Tips having a zero node height will be the latest (i.e., youngest) samples. The root height of this time tree represents the time to the most recent common ancestor (tMRCA) of all samples. To make sure that you selectd the correct option, you can simply look at the Height column. The heights of earlier samples should always be larger than those of later (recent) samples.


Setting the substitution model

We will use the HKY model and estimate base frequencies for all three partitions. To do this, first switch to the Site Model panel, and then choose HKY from the Subst Model drop-boxes, and Estimated from the Frequencies drop-boxes. Also remember to check the estimate checkbox for the Mutation Rate. After three mutation rates are all set to estimate, it will eventually trigger to check the Fix mean mutation rate box.

Figure 6: Site model

Here, we can use Clone function to replicate the configuration. Hold shift key to select all site models on the left side, and click OK to clone the settings from a selected site model (Figure 7). Go through each site model, as you can see, their configurations are same now.

Figure 7: Clone configuration from one site model to others.

The main objective here is to set up the analysis to estimate the relative mutation rates of codons, which are relative to a general rate defined in the molecular clock model.

Molecular clock model

We are going to use a strict clock model in our analyses. This is the simplest clock model that one can use, and it assumes that rates remain constant throughout the whole tree. The strict clock model is also the default clock model in BEAUti, so no changes are necessary in the clock model panel. If you want, you can specify a starting value for the clockRate parameter.


Priors are part of a Bayesian model, and describe our beliefs or prior inferences about the model parameters. Each parameter (including the tree!) is assigned a prior distribution, and this must be done before looking at the data.

To set up the priors, select the Priors tab. We are going to choose a simple tree prior for this analysis, so select Coalescent Constant Population.

For our molecular clock model, we will set the prior on the clockRate parameter to a log-normal distribution with mean of -5, and standard deviation of 1.25 (M=-5 and S=1.25). The plot of this prior distribution and its quantiles can be visualised on the right side.

Figure 8: Priors

The further reading about priors can be seen from the tutorial Prior selection

Setting the MCMC options

For this dataset, let’s initially set the chain length to 1000000 as this will run reasonably quickly on most modern computers. Set the sampling frequencies for the screen logging to 10000, the trace log file to 500 and the trees file to 500. (So, how many samples are we expecting to have in the trace log file?)

Also, change the log file name to RSV2.log and tree log file name to RSV2.trees. If you keep the default tree log file name, $(tree) will be replaced by the name defined at the Tree column in the Partitions panel.

Figure 9: MCMC options

Running BEAST

Save the BEAST .xml specification file (e.g., RSV2.xml).

Figure 10: A screenshot of BEAST.

Now run BEAST and when it asks for an input file, provide your newly created .xml file as input. We recommend you to use BEAGLE library, if it is installed on your machine. BEAST will then run until it has finished reporting information to the screen. The actual results files are saved to disk in the same location as your input file. The output to the screen will look something like this:

                        BEAST v2.6.4, 2002-2021
             Bayesian Evolutionary Analysis Sampling Trees
                       Designed and developed by
 Remco Bouckaert, Alexei J. Drummond, Andrew Rambaut & Marc A. Suchard
                     Department of Computer Science
                         University of Auckland
                   Institute of Evolutionary Biology
                        University of Edinburgh
                    David Geffen School of Medicine
                 University of California, Los Angeles
                      Downloads, Help & Resources:
  Source code distributed under the GNU Lesser General Public License:
                           BEAST developers:
   Alex Alekseyenko, Trevor Bedford, Erik Bloomquist, Joseph Heled, 
 Sebastian Hoehna, Denise Kuehnert, Philippe Lemey, Wai Lok Sibon Li, 
Gerton Lunter, Sidney Markowitz, Vladimir Minin, Michael Defoin Platel, 
          Oliver Pybus, Tim Vaughan, Chieh-Hsi Wu, Walter Xie
                               Thanks to:
          Roald Forsberg, Beth Shapiro and Korbinian Strimmer
         990000     -6059.7592     -5474.8288      -584.9303 1m58s/Msamples
        1000000     -6072.4343     -5468.5631      -603.8711 1m57s/Msamples

Operator                                                   Tuning    #accept    #reject      Pr(m)  Pr(acc|m)
ScaleOperator(StrictClockRateScaler.c:clock)              0.79081       9087      26702    0.03589    0.25390 
UpDownOperator(strictClockUpDownOperator.c:clock)         0.81735        462      35232    0.03589    0.01294 Try setting scaleFactor to about 0.904
ScaleOperator(KappaScaler.s:RSV2_1)                       0.37574        290        885    0.00120    0.24681 
DeltaExchangeOperator(FixMeanMutationRatesOperator)       0.33349       4839      19026    0.02392    0.20277 
ScaleOperator(KappaScaler.s:RSV2_2)                       0.43759        362        849    0.00120    0.29893 
ScaleOperator(KappaScaler.s:RSV2_3)                       0.45654        287        905    0.00120    0.24077 
ScaleOperator(CoalescentConstantTreeScaler.t:tree)        0.73805        287      35575    0.03589    0.00800 Try setting scaleFactor to about 0.859
ScaleOperator(CoalescentConstantTreeRootScaler.t:tree)    0.55544       2111      33800    0.03589    0.05878 Try setting scaleFactor to about 0.745
Uniform(CoalescentConstantUniformOperator.t:tree)               -     192969     166503    0.35885    0.53681 
SubtreeSlide(CoalescentConstantSubtreeSlide.t:tree)       3.65244      30871     148436    0.17943    0.17217 
Exchange(CoalescentConstantNarrow.t:tree)                       -      43760     135429    0.17943    0.24421 
Exchange(CoalescentConstantWide.t:tree)                         -         99      35864    0.03589    0.00275 
WilsonBalding(CoalescentConstantWilsonBalding.t:tree)           -        198      35573    0.03589    0.00554 
ScaleOperator(PopSizeScaler.t:tree)                       0.57008       8862      27174    0.03589    0.24592 
DeltaExchangeOperator(FrequenciesExchanger.s:RSV2_2)      0.06824        434        760    0.00120    0.36348 
DeltaExchangeOperator(FrequenciesExchanger.s:RSV2_1)      0.07452        391        766    0.00120    0.33794 
DeltaExchangeOperator(FrequenciesExchanger.s:RSV2_3)      0.06210        452        761    0.00120    0.37263 

     Tuning: The value of the operator's tuning parameter, or '-' if the operator can't be optimized.
    #accept: The total number of times a proposal by this operator has been accepted.
    #reject: The total number of times a proposal by this operator has been rejected.
      Pr(m): The probability this operator is chosen in a step of the MCMC (i.e. the normalized weight).
  Pr(acc|m): The acceptance probability (#accept as a fraction of the total proposals for this operator).

Total calculation time: 120.762 seconds
End likelihood: -6072.434319012309

Analysing the BEAST output

Drag and drop the BEAST log file RSV2.log to the left panel of the software Tracer. Note that the effective sample sizes (ESSs) for many of the logged quantities are small (ESSs less than 100 will be highlighted in red by Tracer). This is not good. A low ESS means that the trace contains a lot of correlated samples and thus may not represent the posterior distribution well. In the bottom right of the window is a frequency plot of the samples which is expected given the low ESSs is extremely rough.

If we select the tab on the right-hand-side labelled Trace we can view the raw trace, that is, the sampled values against the step in the MCMC chain.

Figure 11: A screenshot of Tracer for a short chain length.

Here you can see how the samples are correlated. There are 2000 samples in the trace (we ran the MCMC for steps sampling every 500) but adjacent samples often tend to have similar values. The ESS for the clockRate is about 18, after removing the first 10% of the samples as burn-in. So we are only getting 1 independent sample to every 100 ~ 1800/18 actual samples). With a short run such as this one, it may also be the case that the default burn-in (10%) of the chain length is inadequate. Not excluding enough of the start of the chain as burn-in will render estimates of ESS unreliable.

The simple response to this situation is that we need to run the chain for longer. Given the lowest ESS (e.g., for the constant coalescent parameter) is 11, it would suggest that we have to run a much longer chain (e.g. 18 times of the current length). But this is a simple dataset, the length of 8 million would generate samples providing the reasonable ESSs that are >200.

So let’s go back to BEAUti, set the chain length to 8000000 and log every 4000 in the MCMC panel. We will also rename the log file to RSV2-long.log and tree log file name to RSV2-long.trees. Then we can create a new BEAST .xml file RSV2-long.xml with a longer chain length. Now run BEAST again and load the new log file into Tracer (you can leave the old one loaded for comparison). Please note BEAST does not support multiple instances from GUI, so you have to close the previous run before you can start a new one.

Click on the Trace tab and look at the raw trace plot.

Figure 12: A screenshot of Tracer for a long chain length.

We have chosen options that produce the same number of samples but with a larger ESS. There is still auto-correlation between the samples but >200 effectively independent samples will now provide a better estimate of the posterior distribution. There are no obvious trends in the plot which would suggest that the MCMC has not yet converged, and there are no significant long range fluctuations in the trace which would suggest poor mixing.

As we are satisfied with the mixing, we can now move on to one of the parameters of interest: the substitution rate. Select clockRate in the left-hand table. This is the average substitution rate across all sites in the alignment. Now choose the density plot by selecting the tab labeled Marginal Density. This shows a plot of the marginal posterior probability density of this parameter. You should see a plot similar to this:

Figure 13: marginal density in tracer

As you can see, the posterior probability density is roughly bell-shaped. There is some sampling noise that would be reduced if we ran the chain for longer or sampled more often, but we already have a good estimate of the mean and HPD interval. You can overlay the density plots of multiple traces in order to compare them (it is up to the user to determine whether they are comparable on the the same axis or not). Select the relative substitution rates for all three codon positions in the table to the left (labelled mutationRate.1, mutationRate.2 and mutationRate.3). You will now see the posterior probability densities for the relative substitution rate at all three codon positions overlaid:

Figure 14: The posterior probability densities for the relative substitution rates

Summarising trees

We will use the program TreeAnnotator to summarise the maximum clade credibility (MCC) tree from the sampled trees (in the tree log file). TreeAnnotator is an application that comes with BEAST. First of all, we need to set the burn-in percentage to 10(%). Then we select the Node heights to Mean heights, and provide input and output file names. This will rescale the node height to reflect the posterior mean node heights for the clades contained in the MCC tree. More details about settings are available from here.

Figure 15: TreeAnnotator for creating a summary tree from a posterior tree set.

Visualising trees

Summary trees can be viewed by FigTree (a program separate from BEAST) and DensiTree (distributed with BEAST). FigTree can only see one tree at a time, so we normally use it to visualise the MCC tree.

Let us open the MCC tree in FigTree. First, open the Trees tab on the left panel, and tick the checkbox Order nodes. Tick the checkbox of the Node Bars tab and open it. By selecting height_95%_HPD, you will be able to display the 95% HPD-intervals on internal node ages (as bars) over each internal node. Then tick the Node labels checkbox, and switch the Display between height (mean) and height_95%_HPD.

In order to interpret the estimated node heights, note that the latest sample in our data set is from 2002. We can thus convert our node heights to years by subtracting their heights from 2002.

Figure 16: The Maximum clade credibility tree for the G gene of 129 RSVA-2 viral samples.

Now load all your posterior trees to DensiTree. Click the show tab and tick the Root Canal checkbox. The “root canal tree”, drawn in thick blue lines, represents the MCC tree. Open the Grid tab, choose Short grid, pick Reverse for the scale axis, and set the Origin to 2002. Please be aware that the origin here means the date of the youngest tips.

Before we can show the 95% HPD interval on the node heights, we need to click the Central button on the top right corner under the type tab.
Then open the Clades tab, set the Smallest text filed to 0.5, to select only the clades with over 50% support. Then, tick the Show clades, and switch draw option from Support to 95%HPD. The error bars representing the 95% HPD interval of internal nodes will now be displayed. If you want to show a particular node, such as the root, you can tick the Select only checkbox, and select it from the panel.

Figure 17: The posterior tree set visualised in DensiTree.


  1. What are the absolute mutation rates for the three codon positions?

  2. In what year did the most recent common ancestor of all RSVA samples live? What is the 95% HPD?

Bonus section: Bayesian Skyline plot

We can reconstruct the population history using the Bayesian Skyline plot. In order to do so, load the .xml file into BEAUti using the menu File => Load. Select the Priors panel and change the tree prior from Coalescent Constant Population to Coalescent Bayesian Skyline. Note that an extra item is added to the priors called Markov chained population sizes which is a prior that ensures dependence between population sizes.

Figure 18: Priors

By default the number of groups used in the skyline analysis is set to

  1. To change this, select menu View => Show Initialization panel, and then a list of parameters is shown in the Initialization panel. Select bPopSizes.t:tree and change the dimension to 3. Likewise, select bGroupSizes.t:tree and change its dimension to 3. The dimensions of the two parameters should be the same. More groups mean more population changes can be detected, but it also means more parameters need to be estimated, and that the MCMC chain must run longer. The extended Bayesian skyline plot automatically detects the number of changes, so it could be used as an alternative tree prior.
Figure 19: Initialization panel

This analysis takes longer to converge, so we will change the MCMC chain length to 10 million, and the log intervals for the trace log and tree log to 10 thousand. Then, save the file and run BEAST. You can also download the log (RSV2-bs.log) and tree (RSV2-bs.trees) files from the precooked-runs directory.

To plot the population history, load the log file in tracer and select the menu Analysis => Bayesian Skyline Reconstruction.

Figure 20: Bayesian Skyline Reconstruction in Tracer

A dialog is shown where you can specify the tree file associated with the log file. Also, since the youngest sample is from 2002, change the entry for age of youngest tip to 2002.

Figure 21: Bayesian Skyline Reconstruction dialog in Tracer

After some calculation, a graph appears showing a population history where the median and 95% HPD intervals are plotted. After selecting the solid interval checkbox, the graph should look something like this.

Figure 22: Bayesian Skyline Reconstruction


  1. By what amount did the effective population size of RSVA grow from 1970 to 2002 according to the BSP?

  2. What are the underlying assumptions of the BSP? Are they violated by this data set?


Change the Bayesian skyline prior to extended Bayesian skyline plot (EBSP) prior and run until convergence. EBSP produces an extra log file, called EBSP.$(seed).log where $(seed) is replaced by the seed you used to run BEAST. A plot can be created by running the EBSPAnalyser utility from AppLauncher, and loading the output file in a spreadsheet.

How many groups are indicated by the EBSP analysis? This is much lower than for BSP. How does this affect the population history plots?

Useful Links

Relevant References

  1. Zlateva, K. T., Lemey, P., Vandamme, A.-M., & Van Ranst, M. (2004). Molecular evolution and circulation patterns of human respiratory syncytial virus subgroup a: positively selected sites in the attachment g glycoprotein. J Virol, 78(9), 4675–4683.
  2. Zlateva, K. T., Lemey, P., Moës, E., Vandamme, A.-M., & Van Ranst, M. (2005). Genetic variability and molecular evolution of the human respiratory syncytial virus subgroup B attachment G protein. J Virol, 79(14), 9157–9167. https://doi.org/10.1128/JVI.79.14.9157-9167.2005
  3. Drummond, A. J., & Bouckaert, R. R. (2014). Bayesian evolutionary analysis with BEAST 2. Cambridge University Press.


If you found Taming the BEAST helpful in designing your research, please cite the following paper:

Joëlle Barido-Sottani, Veronika Bošková, Louis du Plessis, Denise Kühnert, Carsten Magnus, Venelin Mitov, Nicola F. Müller, Jūlija Pečerska, David A. Rasmussen, Chi Zhang, Alexei J. Drummond, Tracy A. Heath, Oliver G. Pybus, Timothy G. Vaughan, Tanja Stadler (2018). Taming the BEAST – A community teaching material resource for BEAST 2. Systematic Biology, 67(1), 170–-174. doi: 10.1093/sysbio/syx060