A Rough Guide to BEAST 1.4

A Rough Guide to BEAST 1.4
A Rough Guide to BEAST 1.4
Alexei J. Drummond1 , Simon Y.W. Ho,
Nic Rawlence and Andrew Rambaut2
Department of Computer Science
The University of Auckland, Private Bag 92019
Auckland, New Zealand
[email protected]
Institute of Evolutionary Biology
University of Edinburgh
Edinburgh, United Kingdom
[email protected]
July 6, 2007
BEAST is a cross-platform program for Bayesian MCMC analysis of molecular
sequences. It is orientated towards rooted, time-measured phylogenies inferred
using strict or relaxed molecular clock models. It is intended both as a method
of reconstructing phylogenies and as a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC
to average over tree space, so that each tree is weighted proportional to its
posterior probability. We include a simple to use user-interface program for
setting up standard analyses and a suite of programs for analysing the results.
There are three main areas of research for which the BEAUti/BEAST package
is particularly applicable. These areas are species phylogenies for molecular dating, coalescent-based population genetics and measurably evolving populations
(ancient DNA or time-stamped viral sequence data sets).
BEAUti (Bayesian Evolutionary Analysis Utility) is a graphical software package that allows the creation of BEAST XML input files. The exact instructions
for running BEAUti differ depending on which computer system you are operating. Please see the README text file that was distributed with the version
you downloaded. Once running, BEAUti will look similar irrespective of which
computer system it is running on.
Importing the NEXUS input file
In the top left hand corner of the BEAUti window is the “File” menu. From
the “File” menu select “Import NEXUS”. A window will appear, allowing you
to select your NEXUS input file.
Data panel
Once your NEXUS input file has been imported into BEAUti, the “Data” window will appear with your sequence information displayed.
Name column
This column contains a unique name for each DNA sequence.
Date column and time-stamped data
This section is only important for researchers interested in ancient DNA (aDNA)
or time-stamped data sets (generally virus sequence data). The “Date” refers
to either the date or the age of the sequences. The default date for all taxa
is assumed to be zero. This will be correct if all your sequences were sampled
at approximately the same time point. For aDNA “Date” will typically be in
radiocarbon years, though for the purposes of analysis, “Date” in years will
suffice. For radiocarbon dates, only enter the absolute date value, not the
associated error. The dates entered need to be specified as “Years” and “Before
the present” from the “Dates specified as” menu. For viral data sets, dates
will most commonly be calendar years (e.g., 1984, 1989, 2007, etc.) or perhaps
months or even days since the start of the study and need to be specified as
“Years”, “Months” or “Days” and “Since some time in the past”. Although
BEAUTi allows you to specify the units for these dates, this is simply to make
a record of the units for reference. Whatever units of time you use to specify
the dates will then be used throughout the analysis. For example, if you give
your dates in millions of years (e.g., 0.1 representing 100,000 years) then all the
reported dates and rates will be given as My (e.g., a reported rate of 0.01 will
have units of substitutions per My and thus be 1.0E-8 substitutions per year).
The “Clear Dates” button resets all dates in the Date column to zero.
Sometimes it is more convenient to use the “Guess Dates” option rather than
enter the date values manually into the “Dates” column. This option guesses
the sequence dates from the numerical information contained within the taxon
name. If the taxon name contains more than one numerical field then you
can specify BEAUti to find the field that corresponds to the sampling date by
specifying the order that the date field comes (first, last, etc.) or specifying a
prefix (a character that comes immediately before the date field in each name).
You can also add a fixed value to each guessed date.
Ancient DNA and radiocarbon dates
Most aDNA data sets will be a combination of dated and undated sequences.
This poses a problem when analysing these data sets with BEAST. There are a
couple of options available to resolve this. It is important however not to leave
a date for any aDNA sequence as zero. BEAST will assume that this sequence
has an age of zero (i.e., is a modern sequence), which will bias the parameter
estimates such as mutation rate, divergence times and population sizes. There
are a few of options:
1. Exclude non-dated aDNA sequences from the analysis. Sequences can be
highlighted in the “Data” window and then deleted by selecting “Delete”
from the “Edit” menu.
2. Have two to three carbon dated aDNA sequences from each sub fossil
deposit, or find published dates for the sub fossil deposit. Use these dates
to calculate an average age for the deposit.
3. If the first two options are not available, the nature of the deposit and
the local geography can be used to calculate an average age of the deposit/aDNA sequences. For example: In New Zealand, sea levels after the
last glaciation did not stabilize at present levels until 6,000 years ago. Once
stabilization occurred (and not before), coastal sand dunes formed, with
swamps forming behind these dunes, trapping moa. Thus the majority of
coastal swamp deposits are less than 6,000 years old, with an average age
of around 3,000 years. Another example is: During the Holocene species
“A” lived in the geographical area “A”. However, from published data we
know that during the height of the last glacial cycle of the Pleistocene
ice age (approximately 20,000-10,000 years ago) species “A” also lived in
the geographical area “B”. Thus any undated sub fossil bones of species
“A” from geographical area “B” have to have an average age of 15,000
years. Finally, glacial U-shaped valleys are an indication that the valley
was under a glacier during the height of the last glacial cycle. These environments did not open up for colonization by plants and animals until
10,000-14,000 years ago. Thus any sub fossil remains found in these valleys have to be younger than 10,000-14,000 years with an average age of
5,000-7,000 years.
Height column
This refers to the height (age) of each sequence relative to the youngest sequence. For non-dated sequences, all heights will be zero. For time-stamped
data BEAUti will designate the youngest (most modern) sequence as height
zero and calculate the age/height of all other dated sequences relative to this.
This information, along with the mutation rate will be used to estimate the
age/height of internal nodes in the tree such as the treeModel.rootHeight
(the age of the root of the tree).
For modern sequences, dates of divergence are set by creating priors on either
internal nodes or the overall rate of substitution.
Sequence column
This column shows the DNA sequence alignment specified in the NEXUS input
Translation options
This is relevant if your analysis concerns protein coding genes and you want to
perform an analysis based on amino acid sequences. If you are analysing noncoding sequence data such as mitochondrial DNA control region, then leave
this option set to “None”. This combo box specifies the amino acid translation
code for the DNA sequence data. The options refer to what organism (e.g.,
“Vertebrate”, “Yeast” or “Bacterial”) and whether the sequence data is nuclear
(for vertebrates select “Universal”) versus mitochondrial (for vertebrates select
“Vertebrate Mitochondrial”). Selecting these options will translate the DNA
sequence into the amino acid sequence using the specified translation code. If
your sequences are amino acid sequences, then this option will be disabled.
Taxa panel
This window allows you to set up taxon subsets within the sequence data. By
setting up these subsets you define a set of taxa. This will also allow you to
log the time to most recent common ancestor tM RCA for each taxon subset
and also set prior distributions on the corresponding divergence times. The
resulting tMRCAs in the log file will be specified in the same units as those
specified for your DNA sequences (i.e., radiocarbon years for aDNA data sets or
for non-dated data sets whatever time units the rate/date priors are specified
in). These taxon subsets can represent different species in multi-species analyses
or perhaps geographically isolated populations within a species. It is important
to note that setting up a taxon subset does not guarantee that this group will
be monophyletic with respect to other taxa in the resulting MCMC analysis.
Therefore the corresponding MRCA may in fact contain other descendants than
just those specified in the taxon set.
You can set up taxon subsets as follows. In the bottom left hand corner
of the screen are a ”+” and ”-” button. Selecting the ”+” button will import
your named sequences into the “Excluded Taxa” column. By selecting specific
sequences, then clicking the right-hand facing arrow, these sequences will be
imported into the “Included Taxa” column and vice versa. The “untitled”
taxon label in the “Taxon Sets” column must be labelled with a specific name
to designate the new taxon subset. Taxon sets can be added by clicking the ”+”
button and removed by clicking the ”-” button. Taxa in the “Included Taxa”
group will be used to date the tM RCA , while taxa in the “Excluded Taxa”
column may or may not be in the same clade.
Model panel
This window allows you to specify DNA or amino acid substitution models,
partition protein coding sequence into codon positions, unlink the substitution
model and rate heterogeneity across codon positions (all parameters can be either shared or made independent among partitions in the sequence data), fix
the substitution rate and finally select the molecular clock model. Depending on whether your data is nucleotide or amino acid sequence (or nucleotides
translated into amino acids) the options will differ. The substitution models
used in BEAST will be familiar to users of other Bayesian and likelihood-based
phylogenetics software.
Substitution model
Substitution models describe the process of one nucleotide or amino acid being
substituted for another. There are two DNA substitution models available in
BEAUti: the Hasegawa-Kishino-Yano (HKY) model and the General Time Reversible (GTR) model. Other substitution models can be achieved by editing
the BEAST XML file generated by BEAUti. For nucleotide data, all the models
that are nested within the GTR model (including the well known HKY85 model)
can be specified by manually editing the XML. When analysing protein coding
data the Goldman and Yang model can be used to model codon evolution [24].
For the analysis of amino acid data the following replacement models can be
used: Blosum62, CPREV, Dayhoff, JTT, MTREV and WAG (ref).
Site heterogeneity model
This allows the refinement of the HKY or GTR model to allow different sites in
the alignment to evolve at different rates. The “None”, “Gamma”, “Invariant
Sites” and “Gamma + Invariant Sites” options in this menu help explain among
site rate heterogeneity within your data.
Selecting “None” specifies a model in which all sites are assumed to evolve
at the same rate. For most data sets, this will not be the case, however for some
alignments there is very little variation and the equal rates across sites model
can’t be rejected.
Selecting “Gamma” will permit substitution rate variation among sites within
your data (i.e., the substitution rate is allowed to vary so that some sites evolve
slowly and some quickly). The shape parameter “alpha” of the Gamma distribution specifies the range of the rate variation among sites. Small alpha values
(< 1) result in L shaped distributions, indicating that your data has extreme
rate variation such that most sites are invariable but a few sites have high substitution rates. High alpha values result in a bell shaped curve, indicating that
there is little rate variation from site to site in your sequence alignment. When
alpha reaches infinity, all sites have the same substitution rate (i.e., equivalent to
“None”). If the analysis concerns protein coding DNA sequences, the estimated
gamma distribution will generally be L-shaped. If the codons are however partitioned into 1st, 2nd and 3rd positions, 1st and 2nd will generally have a lower
alpha value than the 3rd.
Selecting “Invariant Sites” specifies a model in which some sites in your data
never undergo any evolutionary change while the rest evolve at the same rate.
The parameter introduced by this option is the proportion of invariant sites
within your data. The starting value of this parameter must be less than 1.0,
or BEAST will fail to run.
Finally, selecting “Gamma and Invariant Sites” will combine the two simpler models of among-site rate heterogeneity so that there will be a proportion
of invariant sites and the rates of the remaining sites are assumed to be Γdistributed.
Number of Gamma categories
This combo box allows the user to choose the number of categories for the
discrete approximation of the Gamma distribution [25]
Partitioning into codon positions
BEAST provides the ability to analyse multiple data partitions simultaneously
which share or have separate parameters for each partition. If the analysis
concerns just non-coding DNA like mtDNA control region, select “Off” from
the menu. Partitioning is useful when combining multiple genes (e.g., cyt b and
COI), protein and non-coding sequence data (control region and cytochrome
b), and nuclear and mitochondrial data, or to allocate different evolutionary
processes to different regions of a sequence alignment like codon positions. By
partitioning your data, this allows more information from the data set to be
extracted. In BEAUti, you can only partition into 1st, 2nd and 3rd codon
positions. All other partitioning must be done by editing the XML file.
There are two choices. Partitioning into ”(1+2)+3” keeps the 1st and 2nd
positions in one partition (slower substitution rate due to their constrained
nature in coding for amino acids) and the 3rd position in a separate partition
(faster substitution rate due to the increased redundancy in the genetic code of
the 3rd codon position). Partitioning into ”1+2+3” allows each codon position
to have its own substitution rate. This assumes that the data is aligned on
codon boundaries, so that every third site in the alignment is the third position
in a codon for all sequences in the alignment.
Unlinking substitution model across codon positions will instruct BEAST to
estimate a separate transition-transversion ratio (kappa parameter) for HKY or
separate relative rate parameters for GTR for each codon position. Unlinking
rate heterogeneity model will instruct BEAST to estimate the among-site rate
heterogeneity parameters independently for each codon position.
Use SRD06 Model
When this button is pressed BEAUti selects a particular combination of the
above settings which represent the model suggested by Shapiro et al [?]. This
model links 1st and 2nd codon positions but allows the 3rd positions to have a
different relative rate of substitution, transition-transversion ratio and gammadistributed rate heterogeneity. This model has fewer parameters than GTR +
gamma + invariant sites but has been found to provide a better fit for proteincoding nucleotide data.
Fix mean substitution rate
This option is relevant when you have no fossil calibration data and want to
calibrate the data set using a known substitution/mutation rate. This will in
effect calibrate the phylogeny with an external rate and will mean abandoning
any errors associated with this rate. If you want to calibrate your phylogeny
with a rate estimate that includes uncertainty then you should unselect this
option and provide a prior distribution for the rate in the Priors panel (see
section ??).
Setting this to 1.0 will result in the ages of internal nodes being estimated
in units of substitution/site, which is often appropriate when the objective of
the analysis is phylogenetic reconstruction and the time frame of the phylogeny
is not of interest.
Molecular clock rate variation model
This allows you to select the appropriate model for rate variation among branches
in the tree. The model you select will be used to estimate the substitution rate
for each node of the tree, the tM RCA of taxon groups, and the treeModel.rootHeight
parameter (which represents the tM RCA for the root of the phylogeny). There
are currently three options in BEAUti: “Strict Clock”, “Relaxed Clock: Uncorrelated Exponential” and “Relaxed Clock: Uncorrelated Lognormal”. A strict
clock assumes a global clock rate with no variation among lineages in a tree.
However, biology is generally not that simple. Often the data will best fit a relaxed molecular clock model. Relaxed molecular clock models (There are other
models - one under development that will be included in later versions of BEAUti
will be the Random Local Molecular Clock model) assume independent rates on
different branches, with one or two parameters that define the distribution of
rates across branches. The relaxed molecular clock models in BEAST are called
“uncorrelated” because there is no a priori correlation between a lineage’s rate
and that of its ancestor [31].
The strict molecular clock is the basic model for rates among branches supported by BEAST. Under this model the tree is calibrated by either:
1. Specifying a substitution rate (this can be done either by fixing the mean
substitution rate to a designated value or by using a prior on the clock.rate
parameter in the “Priors” panel) or
2. Calibrating the dates of one or more internal nodes (by specifying a prior
on the tM CRA of a taxon subset or the treeModel.rootHeight). This
allows the divergence dates of clades (defined either as a monophyletic
grouping or as the tM RCA of a specified taxon subset) to be calculated
based on the best fit of a single mutation rate across the whole tree.
When using the relaxed molecular clock models, the rate for each branch
is drawn from an underlying exponential or lognormal distribution. We recommend the use of the uncorrelated relaxed lognormal clock as this gives an
indication of how clock-like your data is (measured by the ucld.stdev parameter). If the ucld.stdev parameter estimate is close to 0.0 then the data is
quite clock-like. If the ucld.stdev has an estimated value much greater than
1.0 then your data exhibits very substantial rate heterogeneity among lineages.
This pattern will also be true for the coefficient of variation parameter.
Note: To test MCMC chain performance in the first run of BEAST on a
new data set, it is often a good idea to start with a relatively simple model.
In the context of divergence dating this might mean running a strict molecular
clock (with informative priors on either clock.rate, one or more tM RCA s, or
treeModel.rootHeight). If BEAST can’t produce an adequate sample of the
posterior under a simple model, then it is unlikely to perform well on more
complicated substitution and molecular clock models.
Priors panel
The Priors panel allows the user to specify informative priors for all the parameters in the model. This is both an advantage and a burden. It is an advantage
because relevant knowledge such as fossil calibration points within a phylogeny
can be incorporated into the analysis. It is a burden because when no obvious
prior distribution for a parameter exists, it is your responsibility to ensure that
the prior selected is not inadvertently influencing the posterior distribution of
the parameter of interest.
Tree priors
When sequences have been collected from a panmictic intraspecific population
there are various coalescent tree priors that can be used to model population
size changes through time. Under the coalescent assumption BEAST allows a
number of parametric demographic functions of population size through time:
constant size, exponential growth, logistic growth and expansion growth. Which
one you choose depends on the population you are analysing and the demographic assumptions you wish to make.
In addition, the Bayesian skyline plot (BSP) [29] is available, which calculates the effective breeding population size (Ne ) through time (up to a constant
related to the generation length in the time units of the analysis). However,
the BSP should only be used if the data are strongly informative about population history, or when the demographic history is not the primary object of
interest and a flexible coalescent tree prior with minimal assumptions is desirable. This coalescent-based tree prior only requires you to specify how many
discrete changes in the population history are allowed. It will then estimate
a demographic function that has the specified number of steps integrated over
all possible times of the change-points and population sizes within each step
to calculate a function of Ne through time [29]. Two variants of the BSP are
provided, the “Stepwise” model in which the population is constant between
change-points and then jumps instantaneously and the “Linear” model in which
the population grows or declines linearly between change-points.
All the demographic models listed above are parametric priors on the ages
of nodes in the tree, in which the hyperparameters (e.g., population size and
growth rate in the case of the exponential growth model) can be sampled and
For species-level phylogenies, coalescent priors are generally inappropriate.
In this case, we suggest that you use the Yule tree prior. The Yule tree prior
assumes a constant speciation rate per lineage. This prior has a single parameter
(yule.birthRate) that represents the average net rate of lineage birth. Under
this prior branch lengths are expected to be exponentially distributed with a
mean of yule.birthRate−1 .
UPGMA starting tree
The UPGMA tree is a useful option when you have lots of constraints and priors
on your starting tree that must be satisfied.
Crucial to the interpretation of all BEAST parameters is an understanding of
the units that the tree is measured in. The simplest situation occurs when
no calibration information is available, either from knowledge of the rate of
evolution of the gene region, or from knowledge of the age of any of the nodes
in the tree. If this is the case the rate of evolution is set to 1.0 and the branch
lengths in the tree are then in substitutions per site. However if the rate of
evolution is known in substitutions per site per unit time, then the genealogy
will be expressed in the relevant time units. Likewise, if the age of one or more
nodes (internal or external) are known then this will also provide the units for
the rest of the branch lengths and the rate of evolution. Note that only the set
of parameters that are currently being used (as defined by the model settings)
will be shown in the table. For example, if the rate of substitution is fixed
(in the “Model” section) then the clock.rate parameter (or the ucld.mean
if the relaxed clock is selected) will not be available. With this in mind, the
following table lists some of the parameters that can be generated by BEAUti,
their interpretation and units.
The rate of the strict molecular clock. This parameter only appears when you have selected the strict
molecular clock in the model panel. The units of this
parameter are in substitutions per site per unit time.
If this parameter is fixed to 1.0 (using the Fix mean
substitution rate option in the Model panel) then the
branch lengths in the tree will be in units of substitutions per site. However, if, for example, the tree is
being calibrated by using fossil calibrations on internal
nodes and those fossil dates are expressed in millions
of years ago (Mya), then the clock.rate parameter
will be an estimate of the evolutionary rate in units of
substitutions per site per million years (Myr).
This is the coalescent parameter under the assumption
of a constant population size. This parameter only
appears if you select a constant size coalescent tree
prior. This parameter represents the product of effective population size (Ne ) and the generation length in
units of time (τ ). If time is measured in generations
this parameter a direct estimate of Ne . Otherwise it
is a composite parameter and an estimate of Ne can
be computed from this parameter by dividing it by
the generation length in the units of time that your
calibrations (or clock.rate) are defined in. Finally,
if clock.rate is set to 1.0 then constant.popSize is
an estimate of Ne µ for haploid data such as mitochondrial sequences and 2Ne µ for diploid data, where µ is
the substitution rate per site per generation.
If this value is significantly positive, then it means
that within your phylogeny, branches with fast rates
are followed by branches with fast rates. This statistic measures the covariance between parent and child
branch rates in your tree in a relaxed molecular clock
analysis. If this value spans zero, then branches with
fast rates and slow rates are next to each other. It also
means that there is no strong evidence of autocorrelation of rates in the phylogeny.
exponential.growthRate This is the coalescent parameter representing the rate
of growth of the population assuming exponential growth.
The population size at time t is determined by N (t) =
Ne exp(−gt) where t is in the same units as the branch
lengths and g is the exponential.growthRate parameter. This parameter only appears if you have selected
a exponential growth coalescent tree prior.
This is the parameter representing the modern day
population size assuming exponential growth. Like
constant.popSize, it is a composite parameter unless the time scale of the genealogy is in generations.
This parameter only appears if you have selected a
exponential growth coalescent tree prior.
These five parameters are the relative rates of substitutions for A↔C, A↔G, A↔T , C↔G and G↔T in
the general time-reversible model of nucleotide substitution [22]. In the default set up these parameters are
relative to rC↔T = 1.0. These parameters only appear
if you have selected the GTR substitution model.
This parameter is the transition/transversion ratio (κ)
parameter of the HKY85 model of nucleotide substitution [23]. This parameter only appears if you have
selected the HKY substitution model.
This statistic is logged when a relaxed molecular clock
is use and it is the estimated number of substitutions
per site across the whole tree divided by the estimated
length of the whole tree in time. It has the same units
as clock.rate parameter. If ri is the rate on the ith
branch and ti is the length of time in calendar units for
the ith branch then bi = ri ti is the branch length in
P per site and the meanRate is calculated
as i bi / i ti .
This parameter is the shape (α) parameter of the Γ distribution of rate heterogeneity among sites [25]. This
parameter only appears when you have selected Gamma
or Gamma+Invariant Sites in the site heterogeneity
This parameter is the proportion of invariant sites
(pinv ) and has a range between 0 and 1. This parameter only appears when you have selected “Invariant
sites” or “Gamma+Invariant Sites” in the site heterogeneity model. The starting value must be less than
This parameter represents the total height of the tree
(often known as the tM RCA ). The units of this variable
are the same as the units for the branch lengths in the
tree and will depend on the calibration information for
the rate and/or dates of calibrated nodes.
This is the mean of the branch rates under the uncorrelated lognormal relaxed molecular clock and is
similar but not the same as the mean number of substitutions per site per unit time (see meanRate above).
If ri is
rate on the ith branch then ucld.mean is
i=1 ri and does not take into account the fact
that some branches are longer than others. This parameter can be in real space or in log space depending
on the BEAST XML. However,under default BEAUti
options for the uncorrelated log-normal relaxed clock
this parameter has the same units as clock.rate. If
you want to constrain the mean rate of the relaxed
clock with a prior you should either set a prior on
ucld.mean or meanRate but not both (as they are very
highly correlated via the ri parameters.
This is the standard deviation (σ) of the uncorrelated
lognormal relaxed clock (in log-space). If this parameter is 0 there is no variation in rates among branches.
If this parameter is greater than 1 then the standard
deviation in branch rates is greater than the mean
rate. This is also the case for the coefficient of variation. When viewed in Tracer, if the coefficient of
variation frequency histogram is abutting against zero,
then your data can’t reject a strict molecular clock. If
the frequency histogram is not abutting against zero
then there is among branch rate heterogeneity within
your data, and we recommend the use of a relaxed
molecular clock.
This parameter is the rate of lineage birth in the Yule
model of speciation. If clock.rate is 1.0 then this
parameter estimates the number of lineages born from
a parent lineage per substitution per site. If the tree
is instead measured in, for example, years, then this
parameter would be the number of new lineages born
from a single parent lineage per year.
tmrca(taxon group)
This is the parameter for the tM RCA of the specified
taxon subset that you specified in the “Taxa” panel.
The units of this variable are the same as the units
for the branch lengths in the tree and will depend on
the calibration information for the rate and/or dates of
calibrated nodes. There will a tmrca(taxon group) parameter for each taxon subset specified. Setting priors
on these parameters and/or treeModel.rootHeight
parameter will act as calibration information.
Under the “priors” column is listed the type of prior that is being utilized for
each parameter in the model. Choosing the appropriate prior is important. For
example, if your sequences are all collected from a single time point, then the
overall evolutionary rate must be specified with a strong prior. The units of
the prior will then determine the units of the node heights in the tree, the age
of the MRCA and the units of the demographic parameters such as population
size and growth rate. Depending on the type of prior that you want, there are
several choices. Clicking on the prior for the parameter in question brings up a
menu with the following options. For divergence time estimation, calibrations
are made by placing priors on the treeModel.rootHeight and tmrca(taxon
group) parameters. There are a number of prior distributions that may be
This prior allows you to set up an upper and lower bound on the
parameter. For example you could set an upper and lower bound for
the constant.popSize parameter. The initial value must lie between
the upper and lower bounds.
This prior allows the parameter to select values from a specified
normal distribution, with a specified mean and standard deviation.
Lognormal This prior allows the parameter to select values from a specified
lognormal distribution, with a specified mean and standard deviation (in log units). In addition it is possible to specify a translated
lognormal distribution, so that the whole distribution can have a
lower limit other than 0.0. For example, a fossil that calibrates
the node that cannot be younger than 100 Mya but can be older.
By changing the values of the LogNormal Mean, LogNormal Stdev
(which represent values in log space) and Zero offset you can create a distribution that matches your prior in real space (non-log
space, the values at the bottom of the window). This prior can
also be used on clock.rate and constant.popSize parameters.
This is ideal if you have a small population that is highly genetically structured with deep divergences within the population, as
this can artificially increase the Ne significantly. In addition, Ne
is always an underestimate of the true population size, so a lower
bound is difficult to determine. At present, this prior in BEAUti is
somewhat inconvenient to use as the offset is in units whereas mean
and standard deviation are in log units. This will be improved for
the next version.
Tree prior
If no calibration information is specified through a parametric prior,
a parameter such as treeModel.rootHeight or tmrca(taxon group)
will still have a prior distribution via the selected tree prior. This
option signifies that fact.
In addition there are a number of other priors that can be specified including
Exponential, Gamma and Jeffreys.
Operators panel
Operators act on the given parameters in the BEAST analysis, determining
how the MCMC chain proposes new states to move to. Appropriate choice
of weights and tuning parameter values will allow the MCMC chain to reach
equilibrium/stationary phase faster and sample the target distribution more
efficiently. Each parameter in the substitution model has one or more operators.
A scale operator scales the parameter up or down by a random scale factor, with
the tuning parameter deciding the range of scale factors to choose from. The
random walk operator adds or subtracts a random amount (δ) to or from the
parameter. Again the tuning parameter (window size, w) is used to specify the
range of values that δ can take δ U nif orm(−w/2, w). The uniform operator
simply proposes a new value uniformly within a given range.
On the left hand side of this window is listed the parameters that are going
to be operated including the phylogeny itself which has its own set of operators.
Next to this is the type of operator that will be used.
The tuning column gives the tuning setting to the operator. Some operators
do not have a tuning setting so have a N/A. Changing the tuning setting will
set how large a move that operator will make which will affect how often that
change is accepted by the MCMC algorithm.
The weight column specifies how often each operator is used to propose a new
state in the MCMC chain. Some parameters have very little interaction with
the rest of the model and as a result tend to be estimated very efficiently. An
example of such a parameter is hky.kappa which though efficiently estimated,
requires a complete recalculation of the likelihood of the data whenever it is
changed. Giving these parameters lower weights can improve the computational
efficiency of the run.
The efficiency of the MCMC chain can often be improved by altering the
weight of the operators that work on the treeModel. For example, if you are
analysing x sequences, a very rough rule of thumb is that you should set the
weight of the each of: upDownOperator, uniformOperator on internalNodeHeights,
narrowExchangeOperator, subtreeSlideOperator to x/2. You should also set
the weight of the wilsonBaldingOperator and the wideExchangeOperator to
min(1, x/10). These are rough guidelines and other weights may work better,
but we have found these guidelines to work reasonably well. The authors of
BEAST plan to perform a more systematic study of the performance of different combinations of proposals and weights so we can provide more guidance
in this area in the future. Any assistance in this endeavor would be greatly
The “auto optimize” option will automatically adjust the tuning parameters
of operators as the MCMC algorithm runs, to try to achieve maximum efficiency. We recommend that you choose this option. The criterion that our
auto-optimization method uses is a target acceptance probability for each operator. The target acceptance probability is 0.25 for most operators. The idea
is that proposals for new parameter values should be bold enough that they
explore the parameter space rapidly, without being so large that the proposals
are never accepted. By tuning an operator so that it is accepted 25% of the time
we find that the moves are big enough to explore the space while still allowing
regular changes to the MCMC state.
At the end of the MCMC run, a report on the performance of the operators
will be given in the BEAST console. This report includes the proportion of
times that each operator was accepted, the final values of these tuning settings
and whether they were at the right level for the analysis and suggestions for
changes to these values. These operator tuning parameters can be changed in
order to minimize the amount of time taken to reach optimum performance in
subsequent runs.
Note: changing the tuning parameters of the operators will not change the
results of the analysis - it will only affect the efficiency of the sampling of
the posterior distribution. Better tuning parameters (and weights) will lead to
faster convergence and better mixing of the MCMC chain, which means that
the MCMC run can be run for fewer generations to achieve the same Effective
Sample Size (ESS).
MCMC options
The MCMC panel allows you to set the number of generations the MCMC
algorithm will run for, how often the data is logged to file and to name the
output files that BEAST will store the data in.
Length of chain
This is the number of generations that the MCMC algorithm will run for. The
length of chain depends on the size of the data set, the complexity of the model
and the quality of the sample required. The aim of setting the chain length is
to achieve a reasonable ESS, especially for the parameters of interest. A very
very rough rule of thumb is that for x taxa/individuals you need a generation
time/chain length proportional to x2 . Thus if, for 100 sequences, a 30,000,000
step chain gives good results then for 200 similar sequences we may need a chain
of 120,000,000 steps.
Logging options
The ”Echo state to screen” option determines how often a summary of the state
is outputted to the BEAST console window (e.g., every 1,000 generations). This
option is only important for people that have enough spare time to monitor
BEAST’s progress as it runs ;-). The ”Log parameters every” option determines
how often parameter values are written to the log file (e.g., every 100 generations). Dividing the number of generations in the MCMC chain by the value
specified for ”Log parameters every”, will give you the sample size at the end
of the BEAST run. Ideally, you should aim for a sample size of between 1000
and 10,000 logged parameter values. Logging more samples will simply produce
very large output files which may be difficult to analyse in other programs.
File names
The log file name, tree file name and substitution trees file name determine where
the data that BEAST creates will be saved. The log file will contain the posterior
sample of the parameter values specified in the BEAST XML file. The tree file
will contain a posterior sample of trees (with branch lengths in chronological
units) that can be viewed in TreeView or FigTree. The (subst)tree file will be
a tree file with the branch lengths in units of substitutions. These will be saved
in the same folder that the BEAST XML file is saved under (on UNIX/Linux
systems these files will be saved in the current working directory).
Generating the BEAST input file
Finally, once you are satisfied that you have specified everything you want in the
BEAST XML file, click on the “Generate BEAST File” button in the bottom
right hand corner of the BEAUti window. This will generate an XML file that
can be saved in a specific folder. This is the file that will be used by BEAST to
execute the MCMC analysis.
You can save a separate BEAUti file by selecting the “Save” option from
the “File” menu. It will also be an XML file but will not be recognized by
BEAST, and is only used so that you can re-load it in BEAUti and quickly
make modifications to your analysis at a later date. It is recommended that you
save the BEAUti files with the extension ”.beauti” to distinguish them from the
BEAST input files.
Input format
One of the primary motivations for providing a highly structured XML input
format is to facilitate reproducibility of complex evolutionary analyses. We
strongly encourage the routine publication of XML input files as supplementary
information with publication of the results of a BEAST analysis. Because of the
non-trivial nature of MCMC analyses and the need to promote reproducibility,
it is our view that the publication of the exact details of any Bayesian MCMC
analysis should be made a pre-requisite for publication of all MCMC analysis
Running BEAST
When you open the BEAST software, it will ask you to select your BEAST
XML input file. If the XML file is correct (i.e., no XML or BEAST errors)
then BEAST will run through the various commands and statistics that you
have specified in the BEAST XML file. This will be followed by the pre-burn-in
phase (a defined number of MCMC generations that are discarded at the very
beginning of the MCMC run). During the pre-burn-in phase the operators are
not auto-optimized so as to prevent the operators optimizing incorrectly due
to the very different conditions at the start of the run when the tree is still
random. The * symbols should extend along the dotted line towards towards
100. Once this has occurred, BEAST will start logging to screen and the log
file the posterior sample values given the model specified in the BEAST XML
Note: you should always carry out at least two independent BEAST runs
and then combine the log output files using LogCombiner or TRACER. This
will help increase the ESS of your analysis and also will allow you to determine
if the two independent runs (which will typically have different random starting
trees) are converging on the same distribution in the MCMC run. If they do
not converge on the same distribution then one or both of the runs have failed
to converge on the posterior distribution.
Errors running BEAST
There are two broad types of error messages that appear in the BEAST window:
XML errors and BEAST errors.
XML errors
The most general error message is:
Parsing error - the input file is not a valid XML file
As this error suggests, the file you have selected is probably not even an
XML file. Another possible error message is:
Parsing error - poorly formed XML (possibly not an XML file):
The markup in the document following the root element must be
This error message means that the input file was recognized as an XML
document but it contains a syntax error that prevents the XML file from being
When more detailed XML errors appear, this means that there is something
wrong with the syntax of the XML file, but it is mostly well-formed. This generally only happens if the BEAST XML file has been manually edited. The error
message will usually tell you what the XML error is and what line/character
the error is located. A common XML error occurs when one of the elements
does not have a closing tag (see next section on editing XML). We recommend
that you use an XML-aware editor that will highlight all XML errors like a spell
checker. This will ensure that your XML file is well formed.
BEAST errors
These errors should not occur unless you have edited the XML, in which case
look at the trouble shooting XML section. BEAST errors can occur even if the
XML is well-formed because the XML file may still not describe a valid BEAST
analysis. These errors commonly include:
Spelling mistakes in the parameters names defined in <parameter id="parameterName">
and <parameter idref="parameterName"> which trick BEAST into thinking
that there is a new parameter that has not been previously declared.
The first time you list a parameter it must be have an id attribute. All
subsequent times you reference the same parameter, use the attribute idref
with the same parameter name. Another possible message is:
Parsing error - poorly formed BEAST file
This message means that the input file was recognized as an XML file but
contained some XML elements that BEAST could not understand. Generally
all the details are listed below this error including the line/character number
and what BEAST expected to see. Another common error message is:
Tree likelihood is zero.
This error message means that after considering all the constraints built into
the BEAST XML file, the likelihood of the starting tree is 0 or smaller than the
smallest positive number that can be represented using double precision floating
points numbers. This can happen with data sets containing a large numbers of
sequences (typically > 100). It can also happen if the starting tree does not
conform to some of the tM RCA priors involving upper or lower bounds. There
are a few ways to try to fix this problem. First, in the XML file you can manually
edit the starting values for your parameters to try to achieve a better likelihood
for the starting tree. First and foremost, the initial values of all parameters
have to be within the upper and lower bounds. If you do not explicitly specify
an initial value for a parameter using the value attribute, BEAST will assume
the starting value is zero. This can cause a problem, for example, in the case of
the clock.rate parameter, an initial value of zero will result in a likelihood of
zero for alignments with variable sites.
If this error is occurring because the random initial tree has a likelihood value
that is too close to zero then another alternative is to start with a UPGMA tree.
This can be specified in BEAUti or by manually editing the XML file.
A common cause of this error is if you have specified multiple calibration
bounds in the analysis or a set of monophyly constraints. Often the random
(and even UPGMA) starting trees will not conform to these calibration bounds
or monophyly constraints and this will cause the tree likelihood to be zero.
In this situation you must specify a valid starting tree in Newick format that
conforms to any monophyly or node height constraints that you have specified
(see section ??).
Finally, sometimes BEAST does not proceed through the pre-burn-in period
(i.e., for whatever reason BEAST freezes in the pre-burn-in period). This may
happen if you only have four or fewer taxa in one of your trees.
Troubleshooting BEAST
Sometimes BEAST crashes without any indication why. In these cases you will
need to run BEAST from the command-line in order to get the error message
from the program so that you can report the error message to the BEAST
development team.
Running BEAST from the command line
Open the Command Prompt (or Terminal on Mac OS X) and navigate to the
directory containing the BEAST executable. In Windows type the following
java -jar lib\beast.jar
Alternatively, on Mac OS X, use this command:
java -jar BEAST\ v1.4/Contents/Resources/Java/beast.jar
This will run BEAST as if it was double clicked, however now if BEAST
crashes the crash message (called a stack trace) will be written to the Command
Prompt (Terminal). This will help in diagnosing the problem.
Giving BEAST more memory
Sometimes users try to use very large datasets and BEAST will run out of
memory. In some instances this can be remedied by running BEAST from the
command line ( see section ??) and adding some command line options to direct
JAVA to give BEAST more memory using the -Xms and -Xmx JAVA options.
For example if you wanted to give BEAST 1024Mb of memory you would use
the following command:
java -Xms1024m -Xmx1024m -jar lib\beast.jar
BEAST output and results
At the end of the run report of performance of the operators will be outputted
to the BEAST console window. This table will display the operators, their
acceptance probabilities, the final values of the associated tuning values, and
a textual message indicating whether they were successfully tuned to the right
level for the given data set (low, good, slightly high, too high, etc.). The operator
tuning parameters can be changed either directly in the XML file or in the
“Operator” panel in BEAUti. This level of tuning is optional, as it will not
alter the results that you will get. However it will increase the ESSs in future
The main output from BEAST is a simple tab-delimited plain text file format
(log file) with a row for each sample (the number of rows are dictated by the log
frequency specified in the BEAST XML file). When accumulated into frequency
distributions, this file provides an estimate of the marginal posterior probability
distribution of each parameter. This can be done using any standard statistics
package or using the specially written package, TRACER. TRACER provides
a number of graphical and statistical ways of analysing the output of BEAST
to check performance and accuracy. It also provides specialized functions for
summarizing the posterior distribution of population size through time when a
coalescent model is used (e.g., Bayesian Skyline Plot).
The phylogenetic tree of each sample state is written to a separate file in
either NEWICK or NEXUS format (tree file or (subst)tree file). This can
be used to investigate the posterior probability of various phylogenetic questions, such as the monophyly of a particular group of organisms, or to obtain a
consensus phylogeny.
Opening the BEAST log file in TRACER
It is possible to open the log file in TRACER while BEAST is still running in
order to examine how the run is proceeding. However, the TRACER statistics
will not be updated within TRACER as the run proceeds. To update the results
in TRACER you must re-load the log file.
Editing BEAST XML input files
XML (eXtensible Mark-up Language) is not a file format but is a simple markup
language that can be used to define a file format for a particular purpose. XML
is designed to be easily read by software while still being relatively easy to be
read and edited by humans. BEAST understands a particular file format that
has been defined using the XML markup language.
Generally, in XML, space, tab and new-lines or carriage-returns are all
treated as whitespace. Whitespace characters are used to separate words in
the file but it does not matter how many or in what combination they are used.
Thus parts of the file can be split onto two lines or indented in an arbitrary
BEAST XML format
Below is the structure of the various sections of the XML BEAST file, an explanation of their structure and how to set up the relevant parts of the file. This
is by no means an exhaustive list of what can be incorporated into the BEAST
XML file but will provide a guide to setting one up, and how to partition
data sets into various genes and codon positions and perform a few moderately
complicated analyses. For further information please visit the following link:
http://beast.bio.ed.ac.uk/BEAST XML Reference.
XML files can be constructed in NotePad, WordPad or an XML editor. It is
important however that if XML files are constructed in NotePad or WordPad,
that they are saved as Plain Text files and not Rich Text files. The majority of
the following structural elements can be specified in BEAUti. However, knowing
the structure behind them is an important step towards understanding how
BEAST works.
The following sections are shown in roughly the order that they appear in
the BEAST XML file.
Some general rules of XML and the BEAST format
The BEAST XML file always starts and ends with <beast> and </beast>
The characters < and > are used to bracket the beginning of an element (e.g.,
<beast>). This beginning of an element is called an open tag. The open tag
may have attributes in the form of name="value" pairs (e.g. <mcmc id="mcmc"
chainLength="10000000" autoOptimize="true">). The characters </ and >
are used to signal the end of an element (e.g., </beast>). The end of an element
is called a close tag and cannot have attributes. Between the open and close
tags of an element are its contents. In a BEAST XML file all contents are inside
the beast element. When an element has no contents other than attributes the
characters < and /> can be used instead so that the open and close tags are
merged into one (e.g., <taxon id="Medi"/>).
The attribute id is used to give an element a unique identifier. The attribute
idref is used to refer to a previously defined element that has the corresponding id. An id can be any string of characters but it is customary style to
choose meaningful parameter ids that contain information about what part of
the model the parameter is associated with and what the parameter represents
(e.g treeModel.rootHeight).
All attributes of an element must always be inside double quotes.
Comments can be inserted anyware within the XML file. These comments
will be completely ignored by BEAST. A comment has a special character sequence at the beginning and end.
<!-- This is a comment -->.
Besides describing what different parts of the XML file are for, comments
can be helpful if you want to work out what command is causing a BEAST run
to stall or not work properly. Placing the comment symbols (<!-- and -->)
around an element will cause BEAST to ignore the element. If this is done
systematically for various elements (such as the priors), then it is often possible
to work out which element is causing the error and thereby fix it.
Taxa element/block
The taxa element defines the individuals that the DNA or amino acid sequences
were isolated from. This block links the sequences with the tips of a tree or sequences in different alignments together. The taxa element <taxa id="taxa">
is a unique identifier to reference the taxa that your sequences come from.
The taxa block is where you list the names of your sequences (e.g., <taxon
id="Medi">), dates associated with the sequences, direction the dates are measured in and the units that the dates are in (e.g., <date value="3000.0"
direction="backwards" units="years"/>). In the case of aDNA data sets
the direction is generally backwards (because radiocarbon dates are generally
specified as ages), whereas for viral data sets, which tend to have dates specified
in calendar years (e.g., 1989, 1999, 2006, etc.), the direction is forwards. For
data sets in which all of the sequences are from the same time point the date
element is not necessary. This element is generated in BEAUti from information
in the Data panel.
<taxa id="taxa">
<taxon id="Medi_3000_50">
<date value="3000.0" direction="backwards"
<taxon id="Medi_1000_50">
<date value="1000.0" direction="backwards"
<taxon id="Medi_7000_50">
<date value="7000.0" direction="backwards"
<taxon id="Medi_2000_50">
<date value="2000.0" direction="backwards"
<taxon id="Medi_1000_50">
<date value="1000.0" direction="backwards"
Defining subsets of the taxa element/block
Multiple sets of taxa can be defined by successive taxa elements. Say we have
already defined the chimpanzee and bonobo taxa but want to group them together, so that BEAST creates a tM RCA statistic for this group. This is done
in the following manner. Remember that since you have already defined these
taxon elements previously, you only need to use the idref attribute. Grouping
taxa into a taxa element does not constrain the taxa to be monophyletic in the
BEAST analysis. Defining these taxon subsets can also be done in the “Taxa”
panel within BEAUti.
<taxa id="Pan">
<taxon idref="chimp"/>
<taxon idref="bonobo"/>
Alignment element/block
The alignment element is used to specify the multiple sequence alignment that
will be analysed. Once again you must define the alignment block with a unique
id and also specify the data type (nucleotide, aminoAcid) with the dataType
attribute. Each sequence element should reference a previously defined taxon
element as well as the aligned sequence (with - designating gaps). You can
directly enter the individual nucleotide sequences from an existing alignment
by using the copy/paste function. Alternatively this element will be generated
from information in the “Data” panel of BEAUti.
<alignment id="alignment" dataType="nucleotide">
<taxon idref="Medi_3000_50"/>
<taxon idref="Medi_1000_50"/>
<taxon idref="Medi_7000_50"/>
<taxon idref="Medi_2000_50"/>
<taxon idref="Medi_1000_50"/>
Each alignment block must also have a patterns block. Once again this has
a unique id. It also specifies what region of the alignment the patterns should
be calculated from with the from and to attributes. The patterns element
contains a reference to the corresponding alignment element, indicating that
the patterns we are referring to will be calculated from the specified alignment.
Again BEAUti will generate this element automatically from information in the
“Data” panel.
<patterns id="patterns" from="1" to="383">
<alignment idref="alignment"/>
Demographic model element/block
The demographic model element lets you specify a parametric model of population size to be used as part of the coalescent tree prior. The parametric
models available are constant size, exponential growth, logistic growth and expansion growth. The coalescent tree prior will be used to estimate the size
of a population from a sequence alignment, assuming that the sequences have
been randomly sampled from a single panmictic population. In the example
below we define a constant population size demographic model, which contains
a populationSize element, which in turn contains a parameter element. It is
important to ensure a reasonable starting value for the parameter and appropriate upper and lower bounds. The demographic model element will be generated
based on options in the “Model” panel within BEAUti.
<constantSize id="constant" units="years">
<parameter id="constant.popSize" value="100000.0"/>
Starting tree block/element
This block/element specifies the starting tree to be used in the MCMC run.
Starting with a random starting tree can sometimes lead to difficulties because
the randomly generated tree may not satisfy all of the constraints that have
been placed on it in the form of priors on tree topology and divergence times. If
there are no hard priors (i.e., uniform priors on divergence times or monophyly
constraints), then a random starting tree can be generated using the coalescent
tree prior. The coalescent starting tree block has a unique id. It uses the taxa
and demographic model element that you have specified to construct a random
starting tree. This element will be generated based on options in the “Model”
panel in BEAUti.
<coalescentTree id="startingTree">
<taxa idref="taxa"/>
<constantSize idref="constant"/>
A second option for the starting tree is to start with a UPGMA tree. Again
this can be achieved by selecting the appropriate option in the “Model” panel
in BEAUti. In the XML you can also specify the root height of the tree
(and thus scale the whole UPGMA tree to a certain time scale). The units
of the rate and/or date priors will determine the units of this root height. By
default, the UPGMA tree is constructed using a Jukes-Cantor (ref) distance
matrix constructed from the sequence alignment (specified by the patterns element). If you are making this change manually in the XML file then you
will also have to change the tree model element to make sure that it references the correct starting tree. This is done by deleting the <coalescentTree
idref=‘‘startingTree Medi’’/> command from the tree model block and replacing it with <upgmaTree idref="startingTree"/>
<upgmaTree id="startingTree" rootHeight="25">
<distanceMatrix correction="JC">
<alignment idref="alignment"/>
A third option for the starting tree is to specify a user-defined tree in
NEWICK format. This tree can also be scaled to a different time scale automatically by specifying a rootHeight attribute. Starting with a user-defined
tree is often necessary if there are constraints on the tree imposed by prior
distributions on divergence times or the tree topology. The NEWICK format is used in many programs including PHYLIP and PAML and is embedded within the NEXUS format used by PAUP*. Once again you must delete
<coalescentTree idref="startingTree"/> from the tree model element and
replace it with <newick idref="startingTree"/>.
<newick id="startingTree" units="years">
Finally you can specify a tree using XML format. This will start with an
element representing the tree that contains a node element representing the
root of the tree. This element will in turn contain two or more node elements
representing its descendants, which in turn contain their descendant nodes. A
node that represents a sampled taxon contains no descendant nodes but a taxon
element (or reference to one). This is done by manually editing the XML file:
<tree id="Tree2">
<node><taxon idref="Brazi82"/></node>
<node><taxon idref="ElSal83"/></node>
<node><taxon idref="ElSal94"/></node>
<node><taxon idref="Indon76"/></node>
<node><taxon idref="Indon77"/></node>
TreeModel block/element
This element defines the node height (divergence time) parameters. It contains
a reference to the starting tree element and defines various tree parameters
such as treeModel.rootHeight (the tM RCA of the tree), and internal node
heights. The units of these parameters will be determined by calibration information (i.e., generally years, months or days for serial time-stamped data sets,
or substitutions/site for non-time-stamped data sets without rate/date priors).
BEAUti generates this block automatically.
<treeModel id="treeModel">
<coalescentTree idref="startingTree"/>
<parameter id="treeModel.rootHeight"/>
<nodeHeights internalNodes="true">
<parameter id="treeModel.internalNodeHeights"/>
<nodeHeights internalNodes="true" rootNode="true">
<parameter id="treeModel.allInternalNodeHeights"/>
Bayesian Skyline Plot element/block
This element/block is used when an analysis involves the Bayesian Skyline Plot
tree prior. An upper limit on the population size can be specified by adding
the upper attribute to the skyline.popSize parameter below. This can also
be done from the “Priors” panel in BEAUti. Here is an example of the XML:
<generalizedSkyLineLikelihood id="skyline" linear="false">
<parameter id="skyline.popSize" dimension="5" value="100" lower="0.0" upper="1000.0"/>
<parameter id="skyline.groupSize" dimension="5"/>
<treeModel idref="treeModel"/>
The tM RCA statistic element/block
This element represents the tM RCA for a pre-defined taxon subset. This statistic
represents the divergence time of the node representing the MRCA of the given
taxa (regardless of whether the taxa are monophyletic in the tree). This statistic
thus allows a particular divergence time to be logged even though tree topology
may be changing. By logging this statistic you can obtain a Bayesian posterior
distribution of the divergence of the MRCA of the specified taxa. It has its
own unique id, and the element references the treeModel that will be used to
construct a phylogeny from your data.
<tmrcaStatistic name="time_Pan">
<treeModel idref="treeModel1"/>
<taxa idref="Pan"/>
You can also create a uniform, normal or lognormal prior with appropriate
parameters on a tM RCA statistic and thereby use it as a calibration point. This
can be achieved in the “Priors” panel in BEAUti.
Monophyly statistic block/element
This element represents a Boolean statistic that indicates whether a predefined
taxon subset is monophyletic in the tree. This statistic can be logged in a
BEAST analysis to investigate how frequently the clade is sampled during the
MCMC analysis. It must be done by manual editing the XML file. In the
BEAST run, the monophyly statistic will return a value of 1 if the specified
taxa are monophyletic on the tree and 0 otherwise. The taxa are monophyletic
if there is a node in the tree that has as descendants all the specified taxa and
no others.
<monophylyStatistic id="panMonophyly" name="panMonophyly">
<taxa idref="Pan"/>
<treeModel idref="treeModel1"/>
If you want to constrain a subset of taxa to always be monophyletic in the
tree, then you need to create a booleanLikelihood element. This element
returns a likelihood of 1 (true) if all of the Boolean statistics it contains are
true, otherwise it returns a likelihood of 0 (false). It acts as a multiplier for
the posterior. If the proposed tree satifies all the monophyly constraints then
the likelihood is multiplied by 1, otherwise it is multiplied by 0 and the proposed tree is rejected. The booleanLikelihood element should be added to the
priors element in the mcmc element. You will also have to supply a pre-specified
starting tree that obeys the monophyly constraint for the MCMC analysis to
start successfully.
<booleanLikelihood id="boolean1">
<monophylyStatistic idref="panMonophyly"/>
Coalescent likelihood block/element
This element is used to link a demographic tree prior to a treeModel. This coalescent likelihood of the specified tree given the parameters of the demographic
model, and the divergence times specified by the treeModel parameters in the
populationTree element. BEAUti automatically generates this element when
a coalescent-based tree prior is chosen.
<coalescentLikelihood id="coalescent">
<constantSize idref="constant"/>
<treeModel idref="treeModel"/>
Molecular clock model block/element
This element defines the molecular clock model that will be used to calculate
the likelihood of the tree. For a strict molecular clock there is a single parameter
(the rate of the molecular clock) and the XML element looks like this:
<strictClockBranchRates id="branchRates">
<parameter id="clock.rate" value="1.0E-5"/>
BEAST also allows two models of relaxed molecular clock (uncorrelated
exponential or uncorrelated lognormal). The following XML describes the uncorrelated lognormal relaxed clock:
<discretizedBranchRates id="discreteBranchRates">
<treeModel idref="treeModel"/>
<logNormalDistributionModel id="lnd" meanInRealSpace="true">
<parameter id="lndMean" value="1e-2" lower="0" upper="10"/>
<parameter id="lndStDev" value="1e-3" lower="0" upper="10"/>
<parameter id="rateCategories" dimension="12"/>
For exponentially distributed rates, the XML looks like:
<discretizedBranchRates id="discreteBranchRates">
<treeModel idref="treeModel"/>
<exponentialDistributionModel id="ed">
<parameter id="edMean" value="1e-2" lower="0" upper="10"/>
<parameter id="rateCategories" dimension="12"/>
There are several aspects of the above elements that require some attention.
Firstly, in the lognormal uncorrelated relaxed clock, the meanInRealSpace attribute determines whether the mean of the distribution is described in standard
units (="true"), or log units (="false"). This choice will have an effect on the
implicit prior distribution of this parameter.
If meanInRealSpace="true" then the mean of the lognormal distribution
should be set to some value close to the assumed rate of evolution (that is,
within an order of magnitude). For example for mitochondrial protein-coding
sequences calibrated in millions of years, it might be set to 10−2 . Since one time
unit in this example represents one million years, a rate of 10−2 is equivalent to
1% per million years.
The meanInRealSpace attribute does not apply to the uncorrelated exponential relaxed clock, in which the mean is always in real space. As for the
lognormal distribution, however, it is wise to make a good guess about the
initial value for the mean in order to avoid a long burn-in time.
The number of dimensions for the rateCategories parameter should be set
to the value 2N − 2, where N is the number of taxa in the data set. So, if
there are seven taxa, the rateCategories parameter would have 2 × 7 − 2 = 12
Substitution model element
This element defines the substitution model for your data set. A frequencies
element defines the base pair frequencies with reference to the data type and
the alignment. This element is automatically generated based on choices made
in the “Model” panel of BEAUti. Choosing the HKY substitution will generate
XML that looks like this:
<hkyModel id="hky">
<frequencyModel dataType="nucleotide">
<alignment idref="alignment"/>
<parameter id="hky.frequencies" dimension="4"/>
<parameter id="hky.kappa" value="1.0" lower="0.0" upper="100.0"/>
Selecting the GTR substitution model will generate XML that looks like
<gtrModel id="gtr">
<frequencyModel dataType="nucleotide">
<alignment idref="alignment"/>
<parameter id="gtr.frequencies" dimension="4"/>
<parameter id="gtr.ac" value="1" lower="0" upper="500"/>
<parameter id="gtr.ag" value="1" lower="0" upper="500"/>
<parameter id="gtr.at" value="1" lower="0" upper="500"/>
<parameter id="gtr.cg" value="1" lower="0" upper="500"/>
<parameter id="gtr.ct" value="1" lower="0" upper="500"/>
Site model element
This element defines the among-site rate heterogeneity model for your data (i.e.,
whether you have no rate heterogeneity among sites, gamma-distributed rate
heterogeneity, a proportion of invariant sites, or both gamma-distributed rate
heterogeneity and invariant sites). This element combines the basic substitution
model with the parameters associated with among-site rate heterogenetiy. A
siteModel XML element will be generated similar to the following based on
choices in the “Model” panel in BEAUti:
<siteModel id="siteModel">
<hkyModel idref="hky"/>
<gammaShape gammaCategories="4">
<parameter id="siteModel.alpha" value="1.0" lower="0.0" upper="100.0"/>
<parameter id="siteModel.plnv" value="0.01" lower="0.0" upper="1.0"/>
Tree likelihood element
This element draws together all the components involved in the tree likelihood
calculation. Once again it has a unique treeLikelihood id. Listed under this
parameter are the already defined parameters that will be used to calculate the
treelikelihood. This is automatically done in BEAUti.
<treeLikelihood id="treeLikelihood">
<patterns idref="patterns"/>
<treeModel idref="treeModel"/>
<siteModel idref="siteModel"/>
<strictClockBranchRates idref="branchRates"/>
Partitioning Data
BEAUti provides an easy way to partition your alignment into codon positions
(1st, 2nd and 3rd). However by editing the XML it is also possible to partition
your data into different genes, nuclear versus mitochondrial DNA, coding versus non-coding or even different non-coding regions (HVR1 and HVR2 of the
mtDNA control region). In BEAUti you can partition into codon positions and
unlink substitution model and among-site heterogeneity parameters. Unfortunately, all other data partitioning must be done by manually editing the XML
First of all for multi-locus data you need each locus to have a separate
alignment block. Second, you need to duplicate the patterns element, so that
each partition has a patterns element. For partitioning between codon positions
the from attribute in a patterns element represents the codon position (1, 2 or
3) and the every attribute should be set to ”3”. Below is an example of a multi
locus data set (2 genes/alignments, each split into 3 codon positions for a total
of 6 partitions):
<patterns id="patterns1_E1" from="1" every="3">
<alignment idref="alignment1"/>
<patterns id="patterns2_E1" from="2" every="3">
<alignment idref="alignment1"/>
<patterns id="patterns3_E1" from="3" every="3">
<alignment idref="alignment1"/>
<patterns id="patterns1_E2" from="1" every="3">
<alignment idref="alignment2"/>
<patterns id="patterns2_E2" from="2" every="3">
<alignment idref="alignment2"/>
<patterns id="patterns3_E2" from="3" every="3">
<alignment idref="alignment2"/>
Finally, in order that each partition (e.g., gene, codon position, etc.) can
have independent parameter estimates, the relevant elements have to be duplicated. For example, by duplicating the starting tree and treeModel elements,
you can define a model in which each partition has an independent tree. You
will also have to duplicate the treeLikelihood element for each partition. If
you want to assume a different demographic model for each partition you will
also have to duplicate the demographic model and coalescent likelihood elements. For each duplicated element you will need to create a unique id. Each
partition can have as many or few independent parameters as you like. If the
partitions share a common parameter, you must define the parameter in the first
element that uses it and then use the idref attribute to refer to the parameter
in subsequent elements.
Operators element
The operators element includes all the different types of proposals/moves that
will be made during the MCMC analysis. Failing to specify any operators for a
specific parameter will mean that the parameter will be fixed to its initial value,
because no new values will be proposed. This is one way to fix parameters that
you dont want to estimate (e.g., remove all the operators that act on the tree
if you want to fix the tree topology to the starting tree). Below is an example
of the contents of the operators element. Scale operators, swap operators,
up-down operators and uniform operators all act on the specific parameters
that you have specified in the XML file. Subtree slide, narrow exchange and
wide exchange operators all act on the tree. Each operator has a weight which
determines how often the operator acts on the specified parameter. Most of
this does not need to be changed and automatically generated based on what
you have specified in BEAUti. However, if you are partitioning data then this
becomes more complicated as each partition in the data needs its own set of
<operators id="operators">
<scaleOperator scaleFactor="0.25" weight="1" adapt="false">
<parameter idref="gtr1.ac"/>
<scaleOperator scaleFactor="0.4305" weight="1" adapt="false">
<parameter idref="gtr1.ag"/>
<scaleOperator scaleFactor="0.1853" weight="1" adapt="false">
<parameter idref="gtr1.at"/>
<scaleOperator scaleFactor="0.1853" weight="1" adapt="false">
<parameter idref="gtr1.cg"/>
<scaleOperator scaleFactor="0.1853" weight="1" adapt="false">
<parameter idref="gtr1.gt"/>
<scaleOperator scaleFactor="0.5" weight="1">
<parameter idref="siteModel.alpha"/>
<scaleOperator scaleFactor="0.5" weight="1">
<parameter idref="siteModel.pInv"/>
<scaleOperator scaleFactor="0.5" weight="1">
<parameter idref="yule.birthRate"/>
<scaleOperator scaleFactor="0.9" adapt="false" weight="1">
<parameter idref="lndMean"/>
<swapOperator autoOptimize="false" weight="5" size="1">
<parameter idref="rateCategories"/>
<upDownOperator weight="4" scaleFactor="0.9">
<parameter idref="lndMean"/>
<parameter idref="treeModel.allInternalNodeHeights"/>
<scaleOperator scaleFactor="0.5" weight="1">
<parameter idref="lndStDev"/>
<scaleOperator scaleFactor="0.5" weight="1">
<parameter idref="treeModel.rootHeight"/>
<uniformOperator weight="4">
<parameter idref="treeModel.internalNodeHeights"/>
<subtreeSlide weight="5" gaussian="true" size="1.0">
<treeModel idref="treeModel"/>
<narrowExchange weight="1">
<treeModel idref="treeModel"/>
<wideExchange weight="1">
<treeModel idref="treeModel"/>
MCMC element
This element specifies how the MCMC run will run and the output that will
be produced from the run. You can specify what parameter values you want
to output to file and can also put certain priors on parameters in this element
within the prior element. The first part of this element is concerned with
calculating the prior and posterior probabilities. The next section is concerned
with what parameter values are logged to screen. The final section is concerned
with what parameters are logged to file.
To specify a prior on a parameter you list the type of prior that you want
with the specific associated values (see logNormalPrior below) and within the
element you must refer to the parameter that you want the prior to act on.
This is also where you would add the booleanLikelihood prior for constraining a taxon subset to be monophyletic. Below is an example of the prior
element of the MCMC block. This example defines prior distributions for
the clock.rate, treeModel.rootHeight and siteModel.alpha parameters,
as well as coalescent-based tree priors for three different loci:
<mcmc id="mcmc" chainLength="10000000" preBurnin="30000" autoOptimize="true">
<posterior id="posterior">
<prior id="prior">
<logNormalPrior mean="-15.08" stdev="0.625" offset="0.0">
<parameter idref="clock.rate_Medi"/>
<uniformPrior lower="0.0" upper="1.5E6" offset="0.0">
<parameter idref="treeModel.rootHeight_Pama"/>
<gammaPrior shape="1.0" scale="1.0" offset="0.0">
<parameter idref="siteModel.alpha_Medi"/>
<coalescentLikelihood idref="coalescent_Medi"/>
<coalescentLikelihood idref="coalescent_Pama"/>
<coalescentLikelihood idref="coalescent_Eugr"/>
Here is a simple example of the entire MCMC element:
<mcmc id="mcmc" chainLength="1000000" autoOptimize="true">
<posterior id="posterior">
<prior id="prior">
<distributionLikelihood idref="distributionLikelihood"/>
<speciationLikelihood idref="speciation"/>
<likelihood id="likelihood">
<treeLikelihood idref="treeLikelihood"/>
<operators idref="operators"/>
<log id="screenLog" logEvery="500">
<column label="Likelihood" dp="4" width="12">
<posterior idref="posterior"/>
<column label="Root Height" sf="4" width="12">
<parameter idref="treeModel.rootHeight"/>
<log id="fileLog" logEvery="100" fileName="example.log">
<posterior idref="posterior"/>
<parameter idref="siteModel.alpha"/>
<parameter idref="siteModel.pInv"/>
<parameter idref="gtr1.ac"/>
<parameter idref="gtr1.ag"/>
<parameter idref="gtr1.at"/>
<parameter idref="gtr1.cg"/>
<parameter idref="gtr1.gt"/>
<parameter idref="yule.birthRate"/>
<rateStatistic idref="meanRate"/>
<rateStatistic idref="rateVariance"/>
<rateStatistic idref="rateCoeff"/>
<rateCovarianceStatistic idref="covariance"/>
<parameter idref="treeModel.rootHeight"/>
<tmrcaStatistic idref="hominins-mrca"/>
<tmrcaStatistic idref="primates-mrca"/>
<tmrcaStatistic idref="rodents-mrca"/>
<logTree id="treeFileLog" logEvery="1000" nexusFormat="true" fileName="example.trees">
<treeModel idref="treeModel"/>
Other elements
For help on other aspects of the BEAST XML including:
• Setting up two epoch models
• Running BEAST without data in order sample from the joint prior distribution
• Using nucleotide substitution models other than HKY or GTR and
• Information on the general data type
see the BEAST homepage or http://beast.bio.ed.ac.uk/BEAST XML Reference
for more details.
Tracer is a simple piece of software that can be used for visualization and diagnostic analysis of the MCMC output of BEAST. It reads BEAST (and MrBayes) log files. As with BEAUti and BEAST the exact instructions for running
TRACER differ depending on the type of computer Tracer is being used on.
Once it is running, however, TRACER will look similar irrespective of which
operating system it is running on.
Importing log files into Tracer
Once Tracer has been opened you will see in the top left hand corner the “Trace
Files” panel. Below that will be a ”+” and ”-” button. To load your log
output files press on the + button. A file dialog will appear allowing to choose
a log file to load. Multiple log files can be loaded into Tracer this way and
will be displayed in the “Trace Files” panel. By selecting multiple log files,
you can click the combine button to combine these log files for further analysis.
Combining log files is only appropriate if they represent independent replicates of
the same BEAST analysis. The “Trace Files” panel will also display the number
of generations that the MCMC algorithm ran for and the burn-in period (set by
default to 10% of the MCMC chain length when a log file is loaded into Tracer).
You can double click on the burn-in value to change them if visual inspection
of the trace suggests that 10% is not appropriate.
Analysis using Tracer
On the left hand side of the Tracer window is the name of the log file loaded
and the parameters/statistics that it contains. There will usually be a trace
of the posterior (this is the sum of the log likelihood of the tree, the log prior
probability of the tree and the log prior probability density of any other priors).
There will also be traces of the continuous parameters such as hky.kappa and
treeModel.rootHeight. Selecting a trace on the left brings up a statistical
summary for the trace on the right hand side depending on the tab selected
(see below). For each trace the mean value and the Effective Sample Size (ESS)
will also be displayed. If the ESS is red (it is flagged red if less than 100) then
the MCMC chain has not been run long enough to get a valid estimate of the
parameter. The ESS is an estimate of how many effectively independent draws
from the marginal posterior distribution the MCMC is equivalent to. If this
number is small then the log file may not accurately represent the posterior
distribution and more (or longer) MCMC runs need to be run (see below for
more details).
The basic statistics available for each trace are:
The mean value of the sampled trace across the chain
(excluding values in the burn-in).
The standard error of the estimated mean, taking into
account the ESS, so a small ESS will give a large standard error (stdev of the mean).
The median value of the sampled trace across the chain
(excluding the burn-in).
95% HPD Lower
The lower bound of the 95% highest posterior density
(HPD) interval. The 95% HPD is shortest interval that
contains 95% of the sampled values.
95% HPD Upper
The upper bound of the highest posterior density (HPD)
interval. The 95% HPD is shortest interval that contains
95% of the sampled values.
Auto-correlation time The number of states in the MCMC chain that two samples have to be separated by on average to be regarded
as independent samples from the posterior distribution.
The smaller the ACT the better the MCMC chain is
mixing. The ACT is estimated from the samples in the
trace (excluding the burn-in).
Effective Sample Size The ESS is the number of independent samples from the
marginal posterior distribution that the trace is equivalent to. It is calculated by dividing the chain length
(excluding the burn-in) by the ACT.
You can select a trace and look at the raw trace in the “Trace” panel. This
is the most important step in Tracer analysis. If the Trace for each parameter
has not converged on a stationary distribution (i.e., it looks like a straight hairy
caterpillar with no obvious upward or downward trends or sudden jumps) then
the MCMC run needs to be run for longer. Once the MCMC chain has been run
for long enough the frequency histogram will generally be a smooth unimodal
distribution (although this doesnt have to be the case). Selecting a trace and
displaying it in a density plot will show the posterior probability density of a
parameter. Running the MCMC chain longer can reduce the stochastic noise
in all the plots. You can also select multiple parameters (especially if you have
partitioned data and look at them on the same trace).
Increasing Effective Sample Size
For publication purposes we recommend that all ESS values be greater than
200. If the ESS is small then the estimate of the posterior distribution of that
parameter will be poor and the standard error of the mean of parameter will be
large. Low ESS values are indicative of poor mixing and should cast doubt on the
validity of all parameter estimates in the log file. Trying for ESSs values greater
than 1000 is probably a waste of computational resources. Parameters that
have high ESSs can have their operators down-weighted; whereas parameters
with low ESSs may need their operators weights to be increased.
There are a number of ways to increase the ESSs of your parameters:
• Increase the chain length. This is the most straightforward way of increasing the ESS.
• Combine results from multiple independent runs. We recommend that
you do multiple runs of your analysis and compare results to check that
the chains are converging and mixing adequately, thus they should be
sampling the same distribution and results could be combined (once a
suitable burn-in is removed from each run). The continuous parameters
can be analysed and combined using Tracer. Tree files have to be combined
manually using a text editor.
• Optimize the performance of the operators by using the auto-optimize
option in the Operator panel in BEAUti.
• Increase the sample frequency (if you have less than 1000 samples in total). This may help because the ESS is measuring the correlation between
sampled states in the chain. If the sample frequency is very low each
sample will be independent and ESS will be approximately equal to the
number of states in the log file. Therefore increasing the sample frequency
will increase the ESS. Sampling too frequently will not increase the ESS
but will increase the size of the log file and the time it takes to analyse it.
A balance of these two considerations suggests that sampling so that the
total number of samples is between 1,000 and 10,000 is recommended.
LogCombiner allows you to combine log and tree files from multiple independent
runs of BEAST. When this program is opened the LogCombiner user interface
and a JAVA LogCombiner window will appear.
File Type
This combo menu allows you to select either the log or tree file type that you
will be importing into LogCombiner.
Resample states at lower frequency
This option allows you to resample your posterior distribution at a lower frequency than in previous BEAST runs.
Select input files
Here you can select using the “+” button the input files that you wish to
combine. These will appear in the sub-window with the file name and the
BurnIn peroid (by default 10%).
Output file
This option allows you to select a log file or create a new log file that the
combined log data will be saved to.
When you click “Run”, the log files you have selected will be combined in
the JAVA LogCombiner window. The files you have selected must be from
independent runs of BEAST from the same XML file, otherwise an error will
occur stating that the number of columns in the first file does not match that of
the second file. Once LogCombiner has finished you can analyse the combined
log file in Tracer.
Important: It does not make sense to combine log files from MCMC analyses
of different models or different data sets.
This program assists in summarizing the information from a sample of trees
produced by BEAST onto a single “target” tree. The summary information
includes the posterior probabilities of the nodes in the target tree, the posterior
estimates and HPD limits of the node heights and (in the case of a relaxed
molecular clock model) the rates.
This option allows you to select the amount of burn-in, i.e., the number of
samples that will be discarded at the start of the run, so that you are only
analysing the part of the trace that is in equilibrium.
Posterior probability limit
This is the same as specifying a a limit for bootstrapping in PAUP*. Posterior
summaries will only be calculated for the nodes in the target tree that have a
posterior probability greater than the specified limit.
Target tree type
If you select the “Maximum clade credibility” option then the node height and
rate statistics will be summarized on the tree in the posterior sample that has
the maximum sum of posterior probabilities on its n − 2 internal nodes. This
tree is not necessarily the majority-rule consensus tree.
If you select the “User target tree” then the tree statistics will be summarized
on a user-specified tree. This could, for example, be a majority-rule consensus
tree constructed from the posterior tree sample using PAUP*.
Node heights
This option allows you select how the node heights are summarised on the target
tree. You can choose to keep the heights that the target tree has, or rescale it
to reflect the posterior mean/median node heights for the clades contained in
the target tree.
Target tree file
This option allows you to select and input the target tree. This option will only
be available if you have selected “User target tree” from the “Target tree type”
combo menu.
Input tree file
This option allows you to select the input tree file (the tree file produced by a
BEAST analysis).
Output file
This option allows you to select or create a file that the summarized tree data
will be saved to.
Once you click run, TreeAnnotator will start to summarize the tree data
produced by BEAST. In the JAVA window TreeAnnotator will state the number
of trees that have been read, will find the best fit tree specified under the “Target
tree type”. The progress of this will be monitored by the * symbols moving
across the screen. TreeAnnotator will also give you a clade support statistic
before writing the annotated tree to file. This file can then be analysed in
Authors Contributions
AJD and AR designed and implemented all versions of BEAST up to the current
(version 1.4.2), which was developed between June 2002 and April 2007. Portions of the BEAST source code are based on an original Markov chain Monte
Carlo program developed by AJD (called MEPI) during his PhD at Auckland
University between the years 2000 and 2002. Portions of the BEAST source
code are based on previous C++ software developed by AR. BEAST is now an
open source project and many software developers have made invaluable contributions (see Acknowledgements for some of them). SYWH produced the initial
version of the BEAST XML guide as an online resource. AJD twisted the arm
of NR to bring together the disparate sources of information on BEAST into a
first draft of this manual. All authors contributed to the writing of the text.
We would like to thank (in alphabetical order) Roald Forsberg, Joseph Heled,
Philippe Lemey, Gerton Lunter, Sidney Markowitz, Oliver G. Pybus, Tulio de
Oliveira, Beth Shapiro, Korbinian Strimmer and Mark A. Suchard for invaluable
contributions. AR was supported by the Royal Society.
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