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=Introduction=

Bayesian Inference of Species Trees from Multilocus Data using *BEAST, Alexei J Drummond, Walter Xie and Joseph Heled
April 13, 2012

Provides joint inference of a species tree topology, divergence times, population sizes, and gene trees from multiple genes
sampled from multiple individuals across a set of closely related species. It's an extension of BEAST to *BEAST (pronounced ”star beast”).

Publication:
Joseph Heled and Alexei J. Drummond Bayesian Inference of Species Trees from
Multilocus Data Mol. Biol. Evol. 2010 27: 570-580.

The protocol is taken from the tutorial.

=Protocol=

You will need the following software at your disposal:
* BEAST - this package contains the BEAST program, BEAUti, TreeAnnotator and other utility programs. This tutorial is written for BEAST v1.7.x, which is
available for download from http://beast.bio.ed.ac.uk/.
* Tracer - this program is used to explore the output of BEAST (and other
Bayesian MCMC programs). It graphically and quantitively summarizes the distributions of continuous parameters and provides diagnostic information. At the time of writing, the current version is v1.5. It is available for download from http://beast.bio.ed.ac.uk/.
* 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 v1.3.1. It is available for download from http://tree.bio.ed.ac.uk/.

This tutorial will guide you through the analysis of three loci sampled from 26 individuals representing nine species of pocket gophers. This is a subset of previous published data [1]. The objective of this tutorial is to estimate the species tree that is most probable given the multi-individual multi-locus sequence data. The species tree has 9 taxa, whereas each gene tree has 26 taxa.

*BEAST will co-estimate three gene trees embedded in a shared species tree (see Heled and Drummond, 2010 for details).

== Step 1: Load data in NEXUS format ==

The first step will be to convert a NEXUS file with a DATA or CHARACTERS block into a BEAST XML input file. This is done using the program BEAUti (Bayesian Evolutionary Analysis Utility). This is a user-friendly program for setting the evolutionary model and options for the MCMC analysis. The second step is to actually run BEAST using the input file that contains the data, model and settings. The final step is to
explore the output of BEAST in order to diagnose problems and to summarize the
results.

BEAUti
Run BEAUti and load a NEXUS format alignment, simply select the Import Data... option fromthe File menu.

Select three files called 26.nex, 29.nex, 47.nex by holding shift key. Each file contains an alignment of sequences of from an independent locus.

Once loaded, the three partitions are displayed in the main panel. Double click any alignment (partition) to show its detail.

== Step2: Import or create traits ==

Import trait(s) from a mapping file to fire *BEAST
To enable *BEAST in BEAST v1.7.x, simply click the check box labelled Use species
tree ancestral reconstruction (*BEAST) Heled & Drummond 2010 on the top of
Data Partitions panel. Then, a Create or Import Trait(s) dialog will pop up.

There are two options to be selected:
1. Import trait(s) from a mapping file;
2. Create a new trait and then guess trait value from taxa name species.
Choose the first option and click OK to load the mapping file, named
gopher mapping.txt.
Once loaded, a message indicating the use of *BEAST will be displayed in the status
at the bottom of the window, and a trait named species is created in the trait table
in the Traits tab. Click it to show trait values.

A proper trait file is tab delimited. The first row is always traits followed by the
keyword species in the second column and separated by tab. The rest of the rows
map each individual taxon name to a species name: the taxon name in the first column
and species name in the second column separated by tab. For example:
traits species
taxon1 speciesA
taxon2 speciesA
taxon3 speciesB

For multi-locus analyses, BEAST can link or unlink substitutions models across the
loci by clicking buttons on the top of Data Partitions panel. The default of *BEAST is
unlinking all models: substitution model, clock model, and tree models. Note that you
should only unlink the tree model across data partitions that are actually genetically
unlinked. For example, in most organisms all the mitochondrial genes are effectively
linked due to a lack of recombination and they should be set up to use the same tree
model in a *BEAST analysis.

Alternatively: Create a species trait from taxa names
The advantage of using the Traits panel is that we can extract species names from the
taxa names if they already contain that information. Let’s go to Data Partitions panel
and unselect the check box labelled Use species tree ancestral reconstruction
(*BEAST) Heled & Drummond 2010. As we can see in the status bar on the bottom,
the analysis has been reverted to a standard BEAST analysis.
To enable *BEAST again, click the Use species tree ancestral reconstruction
(*BEAST) Heled & Drummond 2010 on the top of Data Partitions panel, and then
choose the second option in Create or Import Trait(s) dialog this time. Click OK
to continue, and then we will get to Traits panel and click on the Guess trait values
at the top to pop out Guess Trait Value for Taxa dialog. Choose second in the
drop list of Defined by its order, and input as separator. Click OK, and *BEAST
is applied again.

==Step 4: Setting the substitution model==
The next thing to do is to click on the Site Models tab at the top of the main
window. This will reveal the evolutionary model settings for BEAST. Exactly which
options appear depend on whether the data are nucleotides, or amino acids, or binary
data, or general data. The settings that will appear after loading the data set will be
the default values so we need to make some changes.
Most of the models should be familiar to you. For this analysis, we will select each
substitution model listed on the left side in turn to make the following change: select
Empirical for the Base frequencies. Remember to do this for all site models.

7

�Setting the clock model
Second, click on the Clock Models tab at the top of the main window. In this analysis,
we use the Strict Clock molecular clock model as default. Your model options should
now look like this:

8

�The Estimate check box is unchecked for the first clock model and checked for the
rest clock models, because we wish to estimate the mutation rate of each subsequent
locus relative to the first locus whose rate is fixed to 1.0.
Trees
The Trees panel allows priors to be specified for each parameter in the model, which
can be defined on the top of the panel. *BEAST has a different tree prior panel
where users can only configure the species tree prior not gene tree priors (which are
automatically specified by the multispecies coalescent). Currently, we have two species
tree priors: Yule Process and Birth-Death Process; and three population size
models: Piecewise linear and constant root, Piecewise linear, and Piecewise
constant. In this analysis, we use the default options.
The bottom right panel is used to configure the corresponding starting trees. The
Ploidy Type menu determines the type of sequence (mitochondrial, nuclear, X, Y).
This matters since different modes of inheritance give rise to different effective population sizes. The Starting Tree menu provides three options, where the user-specified
starting tree has to be loaded from the data file (e.g. NEXUS file) in advance. In this
analysis, we simply use a random starting tree.

9

�Priors and Operators
The Priors panel allows priors to be specified for each parameter in the model. The
Operators panel is used to configure technical settings that affect the efficiency of the
MCMC program (see Notes for details). We leave these two panels unchanged in this
analysis.
Setting the MCMC options
The next tab, MCMC, provides more general settings to control the length of the
MCMC and the file names.
Firstly we have the Length of chain. This is the number of steps the MCMC will
make in the chain before finishing. The appropriate length of the chain depends on
the size of the data set, the complexity of the model and the accuracy of the answer
required. The default value of 10,000,000 is entirely arbitrary and should be adjusted
according to the size of your data set. For this data set let’s initially set the chain
length to 5,000,000 as this will run reasonably quickly on most modern computers (less
than 20 minutes).
The next options specify how often the parameter values in the Markov chain should
be displayed on the screen and recorded in the log file. The screen output is simply for
monitoring the programs progress so can be set to any value (although if set too small,
10

�the sheer quantity of information being displayed on the screen will actually slow the
program down). For the log file, the value should be set relative to the total length
of the chain. Sampling too often will result in very large files with little extra benefit
in terms of the precision of the analysis. Sample too infrequently and the log file will
not contain much information about the distributions of the parameters. You probably
want to aim to store no more than 10,000 samples so this should be set to no less than
chain length / 10,000.
For this exercise we will set the screen log to 10000 and the file log to 1000. The
final two options give the file names of the log files for the sampled parameters and the
trees. These will be set to a default based on the name of the imported NEXUS file.
If you would like to save the operator analysis into a file, you need to check Create
operator analysis file which will generate a file with the suffix .ops.

• If you are using windows then we suggest you add the suffix .txt to both of these
(so, gopher.log.txt and gopher.trees.txt) so that Windows recognizes these
as text files.
Generating the BEAST XML file
We are now ready to create the BEAST XML file. To do this, either select the Generate BEAST File... option from the File menu or click the similarly labelled button
11

�at the bottom of the window. Check the default priors setting and click Continue.
Save the file with an appropriate name (we usually end the filename with .xml, i.e.,
gopher.xml). We are now ready to run the file through BEAST.

Running BEAST
Now run BEAST and when it asks for an input file, provide your newly created XML
file as input by click Choose File ..., and then click Run.

BEAST will then run until it has finished reporting information to the screen. The
actual results files are saved to the disk in the same location as your input file. The
output to the screen will look something like this:

BEAST v1.7.1, 2002-2012
Bayesian Evolutionary Analysis Sampling Trees
Designed and developed by
Alexei J. Drummond, Andrew Rambaut and Marc A. Suchard

12

�Department of Computer Science
University of Auckland
alexei@cs.auckland.ac.nz
Institute of Evolutionary Biology
University of Edinburgh
a.rambaut@ed.ac.uk
David Geffen School of Medicine
University of California, Los Angeles
msuchard@ucla.edu
Downloads, Help & Resources:
http://beast.bio.ed.ac.uk
Source code distributed under the GNU Lesser General Public License:
http://code.google.com/p/beast-mcmc
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, Chieh-Hsi Wu, Walter Xie
Thanks to:
Roald Forsberg, Beth Shapiro and Korbinian Strimmer

Random number seed: 1334282107812

Parsing XML file: gopher.xml
File encoding: MacRoman
Read alignment: alignment1
Sequences = 26
Sites = 614
Datatype = nucleotide
Read alignment: alignment2
Sequences = 26
Sites = 601
Datatype = nucleotide
Read alignment: alignment3
Sequences = 26
Sites = 819
Datatype = nucleotide
Site patterns ’26.patterns’ created from positions 1-614 of alignment ’alignment1’
pattern count = 144
Site patterns ’29.patterns’ created from positions 1-601 of alignment ’alignment2’
pattern count = 71
Site patterns ’47.patterns’ created from positions 1-819 of alignment ’alignment3’
pattern count = 153
Creating the tree model, ’26.treeModel’
initial tree topology = ((((((((((Thomomys_bottae_bottae,Thomomys_monticola_b),Orthogeomys_heterodus),(Thomomys_talpoides_y
tree height = 0.017
Creating the tree model, ’29.treeModel’
initial tree topology = (((((((((Thomomys_bottae_albatus,Thomomys_bottae_xerophilus),Thomomys_bottae_saxatilis),Thomomys_mo
tree height = 0.016
Creating the tree model, ’47.treeModel’
initial tree topology = (((((((((Thomomys_bottae_cactophilus,Thomomys_bottae_saxatilis),(Thomomys_bottae_mewa,Thomomys_town
tree height = 0.017
Using strict molecular clock model.
Using strict molecular clock model.
Using strict molecular clock model.
Creating state frequencies model ’26.frequencies’: Using empirical frequencies from data = {0.40772, 0.20916, 0.19046, 0.1926

13

�Creating HKY substitution model. Initial kappa = 2.0
Creating site model.
Creating state frequencies model ’29.frequencies’: Using empirical frequencies from data = {0.24545, 0.21384, 0.23701, 0.3037
Creating HKY substitution model. Initial kappa = 2.0
Creating site model.
Creating state frequencies model ’47.frequencies’: Using empirical frequencies from data = {0.2017, 0.21368, 0.21208, 0.37254
Creating HKY substitution model. Initial kappa = 2.0
Creating site model.
Loading native NucleotideLikelihoodCore successfully
TreeLikelihood(26.treeModel) using native nucleotide likelihood core
Ignoring ambiguities in tree likelihood.
With 144 unique site patterns.
Branch rate model used: strictClockBranchRates
TreeLikelihood(29.treeModel) using native nucleotide likelihood core
Ignoring ambiguities in tree likelihood.
With 71 unique site patterns.
Branch rate model used: strictClockBranchRates
TreeLikelihood(47.treeModel) using native nucleotide likelihood core
Ignoring ambiguities in tree likelihood.
With 153 unique site patterns.
Branch rate model used: strictClockBranchRates
Using Yule prior on tree
Likelihood is using -1 threads.
Creating the MCMC chain:
chainLength=5000000
autoOptimize=true
autoOptimize delayed for 50000 steps
# BEAST v1.7.1, r4860
# Generated Fri Apr 13 16:02:42 NZST 2012 [seed=1334282107812]
state Posterior
Prior
Likelihood
PopMean
26.rootHeight 29.rootHeight 47.rootHeight 26.clock.rate
29.clock.rate 47.clock.rate
0 -8271.7599
-408.9912
-7862.7688
1.0000
1.7E-2
1.6E-2
1.7E-2
1.00000
1.00000
1.0000
50000 -4402.6081
-110.7170
-4291.8911
0.0093
2.85227E-2
2.66503E-2
1.70572E-2
1.00000
0.83166
1.
100000 -4282.8572
5.5473
-4288.4044
0.0009
2.49661E-2
3.13122E-2
1.68536E-2
1.00000
0.80206
1
150000 -4321.6989
-32.7608
-4288.9382
0.0025
3.17837E-2
2.81154E-2
1.88322E-2
1.00000
0.83429
1
200000 -4301.2881
-12.5667
-4288.7214
0.0013
2.32344E-2
4.17748E-2
1.82301E-2
1.00000
0.65006
1
250000 -4312.7097
-9.6236
-4303.0861
0.0020
3.12738E-2
2.28171E-2
1.84486E-2
1.00000
0.82719
1
300000 -4278.9322
35.3165
-4314.2487
0.0008
2.27743E-2
2.26837E-2
1.82648E-2
1.00000
1.19420
1
... ...
4900000 -4276.9360
4950000 -4238.0976
5000000 -4304.9506

17.6994
49.0728
-17.2098

-4294.6354
-4287.1704
-4287.7408

0.0018
0.0008
0.0017

2.23113E-2
2.45626E-2
2.47898E-2

1.66667E-2
1.28858E-2
3.78072E-2

1.36587E-2
1.29766E-2
2.25424E-2

Operator analysis
Operator
Tuning
Count
Time
Time/Op
scale(26.kappa)
0.36
1106
122
0.11
scale(29.kappa)
0.383
1097
91
0.08
scale(47.kappa)
0.438
1143
131
0.11
scale(29.clock.rate)
0.485
33005
2668
0.08
scale(47.clock.rate)
0.568
33415
3546
0.11
up:29.clock.rate 47.clock.rate species.yule.birthRate down:speciesTree species.popMean
subtreeSlide(26.treeModel)
0.003
165916
7583
0.05
Narrow Exchange(26.treeModel)
165585
6047
0.04
Wide Exchange(26.treeModel)
33216
988
0.03
wilsonBalding(26.treeModel)
33151
1648
0.05
scale(26.treeModel.rootHeight)
0.511
33209
1527
0.05
uniform(nodeHeights(26.treeModel))
331028
17469
0.05
subtreeSlide(29.treeModel)
0.003
166248
7404
0.04
Narrow Exchange(29.treeModel)
165728
5610
0.03
Wide Exchange(29.treeModel)
33262
887
0.03
wilsonBalding(29.treeModel)
33206
1468
0.04
scale(29.treeModel.rootHeight)
0.447
33466
1530
0.05
uniform(nodeHeights(29.treeModel))
330344
16735
0.05

14

1.00000
1.00000
1.00000

0.91509
1.24784
0.74967

Pr(accept)
0.3698
0.381
0.3596
0.2684
0.2804
speciesTree.splitPopSize nodeHeights(2
0.2326
0.2971
0.0263
0.0379
0.2864
0.5605
0.2201
0.3173
0.0428
0.0473
0.2679
0.5782

�subtreeSlide(47.treeModel)
Narrow Exchange(47.treeModel)
Wide Exchange(47.treeModel)
wilsonBalding(47.treeModel)
scale(47.treeModel.rootHeight)
uniform(nodeHeights(47.treeModel))
up:down:nodeHeights(26.treeModel)
up:29.clock.rate down:nodeHeights(29.treeModel)
up:47.clock.rate down:nodeHeights(47.treeModel)
scale(species.popMean)
scale(species.yule.birthRate)
scale(speciesTree.splitPopSize)
nodeReHeight(sptree,species)

0.002

0.61
0.782
0.766
0.713
0.495
0.24
0.171

165265
165918
33011
33105
33012
332093
32762
33152
32871
54949
33178
1037737
1036242

7854
6217
998
1795
1317
17991
3383
2502
3484
1784
1132
40459
39954

0.05
0.04
0.03
0.05
0.04
0.05
0.1
0.08
0.11
0.03
0.03
0.04
0.04

0.2449
0.2445
0.0141
0.0186
0.2409
0.5415
0.1912
0.1841
0.1732
0.2719
0.3156
0.2572
0.295

6.349283333333333 minutes

Analyzing the results
Run the program called Tracer to analyze the output of BEAST. When the main
window has opened, choose Import Trace File... from the File menu and select the
file that BEAST has created called gopher.log. You should now see a window like the
following:

Remember that MCMC is a stochastic algorithm so the actual numbers will not be
exactly the same.
On the left hand side is a list of the different quantities that BEAST has logged.
There are traces for the posterior (this is the log of the product of the tree likelihood
and the prior probabilities), and the continuous parameters. Selecting a trace on the
15

�left brings up analyses for this trace on the right hand side depending on tab that is
selected. When first opened, the ‘posterior’ trace is selected and various statistics of
this trace are shown under the Estimates tab. In the top right of the window is a table
of calculated statistics for the selected trace.
Tracer will plot a (marginal posterior) distribution for the selected parameter and
also give you statistics such as the mean and median. The 95% HPD lower or upper
stands for highest posterior density interval and represents the most compact interval
on the selected parameter that contains 95% of the posterior probability. It can be
thought of as a Bayesian analog to a confidence interval.
Select the treeModel.rootHeight parameter and the next three (hold shift whilst
selecting). This will show a display of the age of the root and the three gene trees. If
you switch the tab at the top of the window to Marginal Density then you will get
a plot of the marginal posterior densities of each of these date estimates overlayed:

Obtaining an estimate of the phylogenetic tree
BEAST also produces a sample of plausible trees. These need to be summarized using
the program TreeAnnotator (see Notes for details). This will take the set of trees
and identify a single tree that best represents the posterior distribution. It will then
annotate this selected tree topology with the mean ages of all the nodes as well as
the 95% HPD interval of divergence times for each clade in the selected tree. It will
also calculate the posterior clade probability for each node. Run the TreeAnnotator
program and set it up to look like this:
16

�The burnin is the number of trees to remove from the start of the sample. Unlike
Tracer which specifies the number of steps as a burnin, in TreeAnnotator you need
to specify the actual number of trees. For this run, we use the default setting.
The Posterior probability limit option specifies a limit such that if a node is
found at less than this frequency in the sample of trees (i.e., has a posterior probability
less than this limit), it will not be annotated. The default of 0.5 means that only nodes
seen in the majority of trees will be annotated. Set this to zero to annotate all nodes.
For Target tree type you can either choose a specific tree from a file or ask
TreeAnnotator to find a tree in your sample. The default option, Maximum clade
credibility tree, finds the tree with the highest product of the posterior probability
of all its nodes.
Choose Mean heights for node heights. This sets the heights (ages) of each node
in the tree to the mean height across the entire sample of trees for that clade.
For the input file, select the trees file that BEAST created (by default this will
be called gopher.species.trees) and select a file for the output (here we called it
gopher.species.tree).
Now press Run and wait for the program to finish.

Viewing the Tree
Finally, we can look at the tree in another program called FigTree. Run this program,
and open the gopher.species.tree file by using the Open command in the File menu.
The tree should appear. You can now try selecting some of the options in the control
panel on the left. Try selecting Node Bars to get node age error bars. Also turn on
Branch Labels and select posterior to get it to display the posterior probability for
each node. Under Appearance you can also tell FigTree to colour the branches by the
length. You should end up with something like this:

Comparing your results to the prior
Using BEAUti, set up the same analysis but under the MCMC options, select the
Sample from prior only option. This will allow you to visualize the full prior distribution in the absence of your sequence data. Summarize the trees from the full prior
distribution and compare the summary to the posterior summary tree.

References
[1] N.M. Belfiore, L. Liu, and C. Moritz, Multilocus phylogenetics of a rapid radiation in the genus Thomomys (Rodentia: Geomyidae), Systematic Biology 57 (2008), no. 2, 294

STAR BEAST - Revision history

Rf at 18:17, 19 January 2016

Rf at 18:16, 19 January 2016

Rf: Created page with " =Introduction= Bayesian Inference of Species Trees from Multilocus Data using *BEAST, Alexei J Drummond, Walter Xie and Joseph Heled April 13, 2012 Provides joint inferenc..."

@@ Line 166: / Line 166: @@
 actual results files are saved to the disk in the same location as your input file.
 Run the program called Tracer to analyze the output of BEAST. When the main
 window has opened, choose Import Trace File... from the File menu and select the
@@ Line 184: / Line 182: @@
 Select the treeModel.rootHeight parameter and the next three (hold shift whilst selecting). This will show a display of the age of the root and the three gene trees. If you switch the tab at the top of the window to Marginal Density then you will get a plot of the marginal posterior densities of each of these date estimates overlayed:
-== Step 11: Obtaining an estimate of the phylogenetic tree==
+== Step 12: Obtaining an estimate of the phylogenetic tree==
 BEAST also produces a sample of plausible trees. These need to be summarized using
@@ Line 212: / Line 210: @@
 Now press Run and wait for the program to finish.
-==Step 11: Viewing the Tree==
+==Step 13: Viewing the Tree==
 Finally, we can look at the tree in another program called FigTree. Run this program,
@@ Line 222: / Line 220: @@
 length.
+==Step 14: Comparing your results to the prior ==
 Using BEAUti, set up the same analysis but under the MCMC options, select the
 Sample from prior only option. This will allow you to visualize the full prior distribution in the absence of your sequence data. Summarize the trees from the full prior distribution and compare the summary to the posterior summary tree.