Bayesian Phylogenetic Analysis
Exercise written by: Anders Gorm Pedersen
Overview
Today's exercise will focus on phylogenetic analysis using Bayesian methods. We
already discussed Bayesian statistics and Bayesian phylogeny in class, but if you want to
refresh your memory, then you can read a brief introduction at the end of this page.
In this exercise we will explore how one can determine and use posterior probability
distributions over trees, over clades, and over substitution parameters. We will also
touch upon the difference between marginal and joint probability distributions.
Log in to your account.
Construct todays working directory:
mkdir bayes
ls l
Change to today's working directory
cd bayes
Copy files for today's exercise:
cp ~gorm/bayes/primatemitDNA.nexus ./primatemitDNA.nexus
cp ~gorm/bayes/neandertal_aligned_batch.nxs ./neandertal_aligned_batch.nxs
cp ~gorm/bayes/neandertal_aligned.nxs ./neandertal_aligned.nxs
cp ~gorm/bayes/hcvsmall.nexus ./hcvsmall.nexus
cp ~gorm/bayes/mbplot ./mbplot
You have analyzed (versions of) all these data files previously in this course.
We will now use Bayesian phylogenetic analysis to complement what we learned in
those analyses.
Posterior probability of trees
In today's exercise we will be using the program "MrBayes" to perform
Bayesian phylogenetic analysis. MrBayes is a program that, like PAUP*, can be
controlled by giving commands at a command line prompt. In fact, there is a
substantial overlap between the commands used to control MrBayes and the PAUP
command language. This should be a help when you are trying to understand how to
use the program.
Note that the command "help" will give you a list of all available
commands. Issuing "help command" will give you a more detailed
description of the specified command along with current option values. This is
similar to how "command ?" works in PAUP. There is also a very
useful MrBayes Wiki
with a manual and frequently asked questions.
Start program:
mb
This starts the program, giving you a prompt ("MrBayes> ") where you
can enter commands.
Get a quick overview of available commands:
help
Load your sequences:
execute primatemitDNA.nexus
This file contains mitochondrial DNA sequences from 5 different primates. Note that
MrBayes accepts input in nexus format, and that this is the same command that was
used to load sequences in PAUP*. In general, you can use many of the PAUP commands in
MrBayes also.
Inspect data set:
showmatrix
Define outgroup:
outgroup Gibbon
Specify your model of sequence evolution:
lset nst=2 rates=gamma
This command is again very much like the corresponding one in PAUP. You are
specifying that you want to use a model with two substitution types (nst=2),
and this is automatically taken to mean that you want to distinguish between
transitions and transversions. Furthermore, rates=gamma means that you want
the model to use a gamma distribution to account for different rates at different
sites in the sequence.
Start Markov chain Monte Carlo sampling:
Make sure to make the shell window as wide as possible and then issue this
command to start the run:
mcmc ngen=100000 samplefreq=100 nchains=3 diagnfreq=5000
What you are doing here is to use the method known as MCMCMC
("Metropoliscoupled Markov chain Monte Carlo") to empirically determine the
posterior probability distribution of trees, branch lengths and substitution
parameters. Recall that in the Bayesian framework this is how we learn about
parameter values: instead of finding the best point estimates, we typically want
to quantify the probability of the entire range of possible values. An
estimate of the time left is shown in the last column of output.
Let us examine the command in detail. First, ngen=100000
samplefreq=100 lets the search run for 100,000 steps ("generations") and
saves a tree once every 100 rounds (meaning that a total of 1000 trees will be
saved). The option nchains=3 means that the MCMCMC sampling uses 3
parallel chains (but see below): one "cold" from which sampling takes place, and
two "heated" that move around in the parameter space more quickly to find
additional peaks in the probability distribution.
The option diagnfreq=5000 has to do with testing whether the
MrBayes run is succesful. Briefly, MrBayes will start two entirely independent
runs starting from different random trees. In the early phases of the run, the
two runs will sample very different trees but when they have reached convergence
(when they produce a good sample from the posterior probability distribution),
the two tree samples should be very similar. Every diagnfreq
generations, the program will compute a measure of how similar the treesamples
are (specifically, the measure is the average standard deviation of split
frequencies). As a rule of thumb, you may want to run until this value is less
than 0.01.
During the run you will see reports about the progress of the two sets of
four chains. Each line of output lists the generation number and the log
likelihoods of the current tree/parameter combination for each of the two
groups of three chains (a column of asterisks separate the results for the
independent runs). The cold chains are the ones enclosed in brackets [], while
the heated chains are enclosed in parentheses (). Occasionally the chains will
swap so one of the heated chains now becomes cold (and sampling then takes
place from this chain).
Continue run until parallel runs converge on same solution:
At the end of the run, Mrbayes will print the average standard deviation of
split frequencies (which is a measure of how similar the tree samples of the
two independent runs are). We recommend that you continue with the analysis
until the value gets below 0.01 (if the value is larger than 0.01 then you
should answer "yes" when the program asks "Continue the analysis?
(yes/no)".)
Q1: Once you have reached convergence (and answered "no" to continue the
analysis) you should note the total number of generations and the average standard
deviation of split frequencies.
Have a look at the resulting sample files:
Open a new shellwindow and cd to today's working directory. Open one of the parameter sampling files
in an nedit window:
nedit primatemitDNA.nexus.run1.p
This file contains one line for each sampled point (you may want to turn off
linewrapping in nedit under the preferences menu). Each row corresponds
to a certain sample time (or generation). Each column contains the
sampled values of one specific parameter. The first line contains headings
telling what the different columns are: "lnL" is the log likelihood
of the current parameter estimates, "TL" is the tree length (sum of
all branch lengths), "kappa" is the transition/transversion rate ratio,
"pi(A)" is the frequency of A (etc.), and "alpha" is the
shape parameter for the gamma distribution. (Column headings may be shifted relative
to their corresponding columns). Note how the values of most
parameters change a lot during the initial "burnin" period, before they settle
near their most probable values. Now, close the nedit window and have a look at
the file containing sampled trees:
nedit primatemitDNA.nexus.run1.t
Tree topology is also a parameter in our model, and exactly like for the
other parameters we also get samples from treespace. One tree is printed per
line in the parenthetical format used by most phylogeny software. There are 5
taxa in the present data set, meaning that the treespace consists of only 15
different possible trees. Since we have taken more than 15 sample points, there
must be several lines containing the same tree topology.
Examine "burnin":
Recall, that the idea in MCMCMC sampling is to move around in parameter space
in such a way that the points will be visited according to their posterior
probability (i.e., a region with very high posterior probability will be visited
frequently). As mentioned in the lecture it takes a while after starting such a
process before the sampling gets "out of the foothills and into the mountains"
of the distribution, and only after this "burnin" period can the sample points
be trusted. Now,plot the lnL value for one of the run files:
mbplot primatemitDNA.nexus.run1.p 2 0 50000
mbplot is a small script written by me, that extracts the relevant columns
from the .p file and uses gnuplot to produce a plot of how the value changes
with generation number. The options mean that you will get a plot of the lnL
(column 2 in the file) from generation 0 to generation 50,000. Note how the
likelihood value (yaxis) increases through the generations (xaxis) during
the initial burnin, to finally reach a plateau. You can experiment with
plotting other columns as well, or plotting only the initial generations if you want.
Investigate posterior probability distribution over trees:
MrBayes provides the sumt command to summarize the sampled trees. Before
using it, we need to decide on the burnin. Since the convergence diagnostic we
used previously to determine when to stop the analysis discarded the first 25%
of the samples, it makes sense to also discard 25% of the samples obtained
during the analysis.
Return to the shell window where you have MrBayes running. In the command below relburnin=yes and burninfrac=0.25 tells MrBayes to discard 25% of the samples as burnin (you could also have explicitly given the number of samples to discard  help sumt will give you details about the command and the current option settings).
sumt contype=halfcompat conformat=simple showtreeprobs=yes relburnin=yes burninfrac=0.25
(Scroll back so you can see the top of the output when the command is done).
This command gives you a summary of the trees that are in the file you
examined manually above. As mentioned, the option burnin=XX tells
MrBayes how many sample points to discard before analyzing the data. The option
contype=halfcompat requests that a majority rule consensus tree is
calculated from the set of trees that are left after discarding the burnin.
This consensus is the first tree plotted to the screen. Below the consensus
cladogram, a consensus phylogram is plotted. The branch lengths in this have
been averaged over the trees in which that branch was present (a particular
branch corresponds to a bipartition of the data, and will typically not be
present in every sampled tree). The cladogram also has "clade credibility"
values. We will return to the meaning of these later in today's exercise.
What most interests us right now is the list of trees that is printed after
the phylogram. These trees are labeled "Tree 1", "Tree 2", etc, and are sorted
according to their posterior probability which is indicated by a lowercase p
after the tree number. (The uppercase P gives the cumulated probability of
trees shown so far, and is useful for constructing a credible set). This list
highlights how Bayesian phylogenetic analysis is different from maximum
likelihood: Instead of finding the best tree(s), we now get a full list of how
probable any possible tree is.
The list of trees and probabilities was printed because of the option
showtreeprobs=yes. Note that you probably do not want to issue that command
if you have much more than 5 taxa! In that case you could instead inspect the file
named primatemitDNA.nexus.trprobs which is now present in the same directory
as your other files (this file is automatically produced by the sumt
command).
NOTE ADDED APRIL 2013: Apparently there is a bug in the version of mrbayes we are
using now, which means leaf names are not printed on the list of trees with probabilities.
In order to investigate these tree probabilities you will instead need to do the following:
 Copy the file primatemitDNA.nexus.trprobs to your own computer.
 Open the file in a text editor  note that tree probabilitie are included as comments in
this nexus file.
 Also open the file in FigTree. The tree you will see is the first (most probable tree). Reroot on gibbon
and inspect the topology of the tree.
 To see other trees you first need to deselect userrooting (dont know why this is so...):
In the leftmost column of the figtree window, open the "Trees" section, and deselect the
"Root tree" checkbox.
 When user rooting has been deselected you can click the arrow labeled "Prev/Next" at the
top of the window to move to the next (less probable) tree. Again root on gibbon and inspect topology.
 Deselect user rooting again, move to the final tree, root on gibbon and inspect topology.
Report results:
Q2: Include a plot of the most probable tree and note its posterior
probability. How did the less probable trees differ from the most likely one?
Analysis of Neanderthal data (posterior probability of clades)
The classical view emerging from anatomical and archaeological studies places
Neanderthals as a different species from Homo sapiens. This is in agreement with
the "OutofAfrica hypothesis", which states that Neanderthals coexisted without
mating with modern humans who originated in Africa somewhere between 100,000 to
200,000 years ago. There is, however, also anatomical and paleontological
research which supports the socalled "multiregional hypothesis", which
propounds that some populations of archaic Homo evolved into modern human
populations in many geographical regions. Consequently, Neanderthals could have
contributed to the genetic pool of presentday Europeans. We will use the
present data set to consider this issue.
Load Neanderthal data set:
execute neandertal_aligned.nxs
Investigate data:
showmatrix
The data set you have analyzed here consists of an alignment of mitochondrial DNA
from human (53 sequences), chimpanzee (1 sequence), and Neanderthal (1
sequence). The Neanderthal DNA was extracted from archaeological material,
specifically bones found at Vindija in Croatia.
Start analysis:
outgroup Pan_troglodytes
lset nst=2
mcmc ngen=200000 nchains=3 diagnfreq=10000
Find posterior probability of clades:
sumt contype=halfcompat showtreeprobs=no relburnin=yes burninfrac=0.25
Examine the consensus tree that is plotted to screen: On the branches
that are resolved, you will notice that numbers have been plotted. These are
cladecredibility values, and are in fact the posterior probability that the
clade is real (based on the present data set). These numbers are different from
bootstrap values: unlike bootstrap support (which have no clear statistical
meaning) these are actual probabilities. Furthermore, they have been found
using a full probabilistic model, instead of neighbor joining, and have still
finished in a reasonable amount of time. These features make Bayesian phylogeny
very useful for assessing hypotheses about monophyly.
Q3: Also investigate the tree in FigTree: the result of the sumt command has been
saved to a file named "neandertal_aligned.nxs.con.tre". Open this file in FigTree, root the
tree on the chimpanzee, and select display of clade credibilities by putting a check mark in the box
"Branch labels", expanding the "Branch labels" section by clicking on the disclosure triangle, and
selecting "prob" in the "Display" dropdown menu. Include the plot in your report.
Q4: Based on your values, how much support does the present data set
give to the outofAfrica hypothesis? (Are Homo sapiens a monophyletic group
excluding the Neanderthal?). Note the clade credibility for Homo sapiens.
Probability distributions over other parameters
As the last thing today, we will now turn away from the tree topology, and
instead examine the other parameters that also form part of the probabilistic
model. We will do this using a reduced version of the Hepatitis C virus data
set that we have examined previously. Stay in the shell window where you
just performed the analysis of Neanderthal sequences.
Load data set:
execute hcvsmall.nexus
Define site partition:
charset 1stpos=1.\3
charset 2ndpos=2.\3
charset 3rdpos=3.\3
partition bycodon = 3:1stpos,2ndpos,3rdpos
set partition=bycodon
prset ratepr=variable
This is an alternative way of specifying that different sites have different
rates. Instead of using a gamma distribution and learning which sites have what
rates from the data, we are instead using our prior knowledge about the
structure of the genetic code to specify that all 1st codon positions have the
same rate, all 2nd codon positions have the same rate, and all 3rd codon
positions have the same rate. Specifically, charset 1stpos=1.\3 means
that we define a character set named "1stpos" which includes site 1 in the
alignment followed by every third site ("\3", meaning it includes sites
1, 4, 7, 11, ...) until the end of the alignment (here denoted ".").
Specify model:
lset nst=6
This specifies that we want to use a model of the General Time Reversible (GTR)
type, where all 6 substitution types have separate rate parameters.
When the lset command was discussed previously, a few issues were
glossed over. Importantly, and unlike PAUP, the lset command in MrBayes gives
no information about whether nucleotide frequencies are equal or not, and
whether they should be estimated from the data or not. In MrBayes this is
instead controlled by defining the prior probability of the nucleotide
frequencies (the command prset can be used to set priors). For
instance, a model with equal nucleotide frequencies corresponds to having prior
probability 1 (one) for the frequency vector (A=0.25, C=0.25, G=0.25, T=0.25),
and zero prior probability for the infinitely many other possible vectors. As
you will see below, the default prior is not this limited, and the program will
therefore estimate the frequencies from the data.
Inspect model details:
showmodel
This command gives you a summary of the current model settings. You will also get a
summary of how the prior probabilities of all model parameters are set. You will for
instance notice that the nucleotide frequencies (parameter labeled "Statefreq")
have a "Dirichlet" prior. We will not go into the grisly details of what exactly the
Dirichlet distribution looks like, but merely note that it is a distribution over many
variables, and that depending on the exact parameters the distribution can be more or
less flat. The Dirichlet distribution is a handy way of specifying the prior probability
distribution of nucleotide (or amino acid) frequency vectors. The default statefreq prior
in MrBayes is the flat or uninformative prior dirichlet(1,1,1,1).
we will not go into the priors for the remaining parameters in any detail, but you may
notice that by default all topologies are taken to be equally likely (a flat prior on
trees).
Start MCMC sampling:
mcmc ngen=300000 samplefreq=100 diagnfreq=10000 nchains=4
The run will take about 2 minutes to finish (you may want to ensure that the
average standard deviation of split frequencies is less than 0.01 before ending
the analysis).
Compute summary of parameter values:
sump relburnin=yes burninfrac=0.25
The sump command works much like the sumt command for the
nontree parameters. Again, we are using 25% of the total number of samples as burnin.
First, you get a plot of the lnL as a function of generation number. Values
from the two independent runs are labeled "1" and "2" respectively. If your
burnin has been chosen well, then the points should be randomly scattered over
a narrow lnL interval. (If you plotted lnL for the entire run, you would get
a plot similar to what you got with mbplot earlier, where the lnL rises
during the burnin and then levels off).
Secondly, the posterior probability
distribution of each parameter is summarized by giving the mean, variance,
median, and 95% credible interval.
Q5: Based on the reported posterior means, does it seem that
r(C<>G) is different from r(A<>C)?. Report the mean of both
parameters. Here r(C<>G) is the relative
substitution rate between C and G.
Marginal vs. joint distributions:
Strictly speaking the comparison above was not entirely appropriate. We first found the overall distribution
of the r(C<>G) parameter and then compared its mean to the mean of the overall distribution of the
r(A<>C) parameter. By doing things this way, we are ignoring the possibility that the two parameters
might be associated in some way. For instance, one parameter might always be larger than the other in any
individual sample, even though the total distributions have a substantial overlap. We should instead be looking
at the distribution over both parameters simultaneously. A probability distribution over several parameters
simultaneously is called a "joint distribution" over the parameters.
By looking at one parameter at a time, we are summing its probability over all values of
the other parameters. This is called the marginal distribution. The figure below
illustrates a joint distribution over two parameters, and shows how one can think of the
marginal distribution as what you get when you view the joint distribution from the side,
so to speak.
Joint vs. marginal distribution. (Figure from Berry, "Statistics  a Bayesian perspective".)
Examine marginal and joint distributions:
Again find a shell window where MrBayes is not running and issue the following command:
cat hcvsmall.nexus.run1.p  grep v '^Gen'  gawk '{if (NR > 750) print $7}' > rCG.data
This command takes the parameter file, removes the header line, and for all lines that were sampled after
the burnin period (set to 750 here) it prints the r(C<>G) parameter to the file named "rCG.data".
Now extract a few extra interesting columns in a similar manner.
cat hcvsmall.nexus.run1.p  grep v '^Gen'  gawk '{if (NR > 750) print $4}' > rAC.data
cat hcvsmall.nexus.run1.p  grep v '^Gen'  gawk '{if (NR > 750) print $14}' > rm1.data
cat hcvsmall.nexus.run1.p  grep v '^Gen'  gawk '{if (NR > 750) print $15}' > rm2.data
cat hcvsmall.nexus.run1.p  grep v '^Gen'  gawk '{if (NR > 750) print $16}' > rm3.data
We can now plot some interesting distributions (Note: you may need to use a shell
window on organism.cbs.dtu.dk or login.cbs.dtu.dk for the "histo" program to work):
histo w 0.01 L"CG":"AC" m ll rCG.data rAC.data
Q6: Include the plot in your report.
We here use the histo program to plot the marginal distributions of rCG and rAC (issue
the command man histo if you want to learn how to use the histo program). Note
that while rAC tends to be larger than rCG, there is still a substantial overlap between
the distributions. We therefore need to examine the joint distribution to find the real
probability that rAC > rCG. Close the plot window and issue this command:
wc l rCG.data
Q7: This gives us a count of how many points remain in the sample files
after having discarded the burnin. Write down the number.
paste rAC.data rCG.data  gawk '{if ($1>$2) print $0}' Â wc l
Q8: This counts the number of sample points where rAC > rCG (note this
number also). You can now find the joint probability that rAC > rCG by
dividing the last number by the first (do this and note the result).
Note how examining the joint distribution provides you with information that you
could not get from simply comparing the marginal distributions. This very simple procedure can also be
performed using spreadsheet programs, and can obviously be used to answer many different questions.
We will finish off today's exercise by making one final plot:
histo w 0.01 L"1st":"2nd":"3rd" X "Relative substitution rate" m lll rm1.data rm2.data rm3.data
Q9: This command plots the relative substitution rates at the first, second, and third codon positions. Since random mutations presumably hit all three codon positions with the same frequency, any differences must be caused by subsequent selection. How does the result fit with your knowledge of the
structure of the genetic code? Include the plot in your report.
Resources
 MrBayes:
Bayesian Inference of Phylogeny.
As was the case for likelihood methods, Bayesian analysis is founded on having a
probabilistic model of how the observed data is produced. (This means that, for a given
set of parameter values, you can compute the probability of any possible data set). You
will recall from today's lecture that in Bayesian statistics the goal is to obtain a full
probability distribution over all possible parameter values. To find this socalled
posterior probability distribution requires combining the likelihood and the prior
probability distribution.
The prior probability distribution shows your beliefs about the parameters before
seeing any data, while the likelihood shows what the data is telling about the
parameters. Specifically, the likelihood of a parameter value is the probability of
the observed data given that parameter value. (This is the measure we have previously
used to find the maximum likelihood estimate). If the prior probability distribution
is flat (i.e., if all possible parameter values have the same prior probability) then
the posterior distribution is simply proportional to the likelihood distribution, and
the parameter value with the maximum likelihood then also has the maximum posterior
probability. However, even in this case, using a Bayesian approach still allows one to
interpret the posterior as a probability distribution. If the prior is NOT flat, then
it may have a substantial impact on the posterior although this effect will diminish
with increasing amounts of data. A prior may be derived from the results of previous
experiments. For instance one can use the posterior of one analysis as the prior in a
new, independent analysis.
In Bayesian phylogeny the parameters are of the same kind as in maximum likelihood
phylogeny. Thus, typical parameters include tree topology, branch lengths, nucleotide
frequencies, and substitution model parameters such as for instance the
transition/transversion ratio or the gamma shape parameter. (The difference is that
while we want to find the best point estimates of parameter values in maximum
likelihood, the goal in Bayesian phylogeny is instead to find a full probability
distribution over all possible parameter values). The observed data is again usually
taken to be the alignment, although it would of course be more reasonable to say that
the sequences are what have been observed (and the alignment should then be inferred
along with the phylogeny).
