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Unit 18 Bayesian Phylogenetics

download and install tracer on your computer [LINK]

qlogin with 1 processor

mkdir ~/unit18
cd ~/unit18

Download the data

wget -O primates_alnMuscle.nex

We are using the same primate data from last week (1 Cytb sequence per genus for all (available) primates). The only difference is the data has been structured according to the nexus format. The file now looks like this:


begin data;
    dimensions nchar=1140 ntax=50;
    format missing=? gap=- matchchar=. datatype=DNA;
        Allocebus_trichotis.AY441461            ATGACCAACATTCGAAAAATCCACCCCTTAATAAAAG...
        Alouatta_belzebul.AY374353.2            ATGACTACCCCCCGCAAAACACACCCACTAGCAAAAA...
        Aotus_azarai.DQ098863                   ATGACTTCNCCCCGCAAAACACACCCACTAGCAAAGA...
        Arctocebus_calabarensis.AY441474        ATGACAATTATACGAAAACAACACCCATTAGCAAAAA...
         Varecia_variegata.AF081047             ATGACTAACACCCGAAAAAACCACCCTCTAATAAAAA...
        Tupaia_belangeri.EU531776               ATGACCAACATACGAAAAAACCACCCATTACTAAAGA...

A nexus file always begins with #NEXUS and data is organised in blocks. Here all of the sequence data is given in a single data block. The data block begins with begin data; and ends with end;. It is further broken down into specific commands that end with a ;. For example. The dimensions command has two options nchar and ntax. The format command has options for missing data, gap coding, matchchar (matching character) coding, and datatype. All of the sequences are in the matrix section that ends with the ; after the last seqeunce.

I absolutely DESPISE entiring commands directly into mrbayes. I keep two windows (or panels...if you are cool and use tmux) open to work with MrBayes. Open another window. You do not need to qlogin for this additional window.

cd unit18
nano primates_alnMuscle.nex

Now you can edit your file in this window while running it in the other.

2) Running mrbayes

mpirun -np 1 mb

Execute your data within mrbayes:

execute primates_alnMuscle.nex

This loads our sequence data into mrbayes. So how to build a tree?.


This gives us all of the commands that are available in mrbayes. Look through them. If you need parameter options for each command then type help <command. For example, to find out about the Monte-Carlo Markov Chain parameters you could use:

MrBayes > help mcmcp

   This command sets the parameters of the Markov chain Monte Carlo (MCMC)       
   analysis without actually starting the chain. This command is identical       
   in all respects to Mcmc, except that the analysis will not start after        
   this command is issued. For more details on the options, check the help       
   menu for Mcmc.
   Parameter       Options               Current Setting                         
   Ngen            <number>              1000000                                      
   Nruns           <number>              2                                      
   Nchains         <number>              4                                      
   Temp            <number>              0.100000                                     
   Reweight        <number>,<number>     0.00 v 0.00 ^                       
   Swapfreq        <number>              1                                      
   Nswaps          <number>              1                                      
   Samplefreq      <number>              500                                      
   Printfreq       <number>              1000                                      
   Printall        Yes/No                Yes                                      
   Printmax        <number>              8                                      
   Mcmcdiagn       Yes/No                Yes                                      
   Diagnfreq       <number>              5000                                      
   Diagnstat       Avgstddev/Maxstddev   Avgstddev                                     
   Minpartfreq     <number>              0.10                                 
   Allchains       Yes/No                No                                     
   Allcomps        Yes/No                No                                     
   Relburnin       Yes/No                Yes                                     
   Burnin          <number>              0                                     
   Burninfrac      <number>              0.25                                 
   Stoprule        Yes/No                No                                     
   Stopval         <number>              0.05                                 
   Savetrees       Yes/No                No                                     
   Checkpoint      Yes/No                Yes                                     
   Checkfreq       <number>              2000                                     
   Filename        <name>                primates_alnMuscle.nex.<p/t>
   Startparams     Current/Reset         Current                                     
   Starttree       Current/Random/       Current                                     
   Nperts          <number>              0                                     
   Data            Yes/No                Yes                                     
   Ordertaxa       Yes/No                No                                     
   Append          Yes/No                No                                     
   Autotune        Yes/No                Yes                                     
   Tunefreq        <number>              100                                     

From this help menu not only do you get an idea of parameters, but you can find out what the default/current settings are. In this case, we would run two independent mcmc runs with 1M generations and four chains sampling every 500 generations.

To change a parameter:

mcmcp nruns=1

Now set the bayes run to

mcmcp ngen=10000 samplefreq=100 printfreq=1000 filename=primates_10K-100s-1r-4c.bayes

Now execute the MCMC run.


This should run in less than 5 minutes. While its running we can discuss what is actually going on and what all of the output means.

The MCMC sampler will use the following moves:
      With prob.  Chain will use move
         0.98 %   Dirichlet(Pi)
         0.98 %   Slider(Pi)
         9.80 %   ExtSPR(Tau,V)
         9.80 %   ExtTBR(Tau,V)
         9.80 %   NNI(Tau,V)
         9.80 %   ParsSPR(Tau,V)
        39.22 %   Multiplier(V)
        13.73 %   Nodeslider(V)
         5.88 %   TLMultiplier(V)
Chain results (100000 generations requested):
 0 -- [-47508.809] (-46388.062) (-46133.286) (-46633.652) (...0 remote chains...) 
 5000 -- [-28952.864] (-28977.447) (-28943.149) (-28960.214) (...0 remote chains...) -- 0:01:16
 10000 -- (-28976.122) [-28958.717] (-28961.856) (-28962.195) (...0 remote chains...) -- 0:01:12
 15000 -- (-28964.889) (-28947.323) (-28959.086) [-28970.997] (...0 remote chains...) -- 0:01:13
 20000 -- (-28952.999) (-28968.473) [-28949.931] (-28970.990) (...0 remote chains...) -- 0:01:08

Other terms to be aware of:

  • chains (hot vs. cold)
  • swap
  • temp/heating
  • runs

So this has generated ngens / samplefreq trees that have been saved into the primates_100K-100s-1r-4c.bayes.t and parameters have been saved in primates_100K-100s-1r-4c.bayes.p.

3) Summarizing the run

Lets look at the distribution of our data. Download the primates_100K-100s-1r-4c.bayes.p file and open it in

Here we can see if our data reached stationarity wihtin the ranges of our MCMC parameters. You can really see the importance of setting an approporiate burnin here.

Based on the tracer visualizaiton, we know that we need to burnin the first trees and ultimatley increase the number sampled. We will increase our sampling in a bit, but for know we just want to know what our tree looks like...but we generated more than 1K. All trees can be summarized via the following:


Why did we not include a burnin when summing the trees?

The files that were generated include

  • primates_10K-100s-1r-4c.bayes.vstat - length information
  • primates_10K-100s-1r-4c.bayes.trprobs - topologies in order of posterior probability
  • - partition frequencies (of the consensus tree)
  • primates_10K-100s-1r-4c.bayes.con.tre - consensus tree.

The model parameteres and tree stats can be summarized using:


In this version of mrbayes there is a lot of overlap in the information generated here and with Tracer.

Files generated are:

  • primates_10K-100s-1r-4c.bayes.pstat - model parameter statistics
  • primates_10K-100s-1r-4c.bayes.lstat - likelihood statistics

To this point you have given mrbayes commands via the CLI. It is much easier to add the commands to a mrbayes block in your nexus file. Lets re-run the analysis using a mrbayes block while adding some new commands.

4) Running a more complex analysis via the #Nexus file

Add the following to the end of the primate_muscleAln.nex.

begin mrbayes;

        [set outgroup]
                outgroup Tupaia_belangeri.EU531776;

        [designate character sets]
        charset cytbPos1 = 1-1140\3;
        charset cytbPos2 = 2-1140\3;
        charset cytbPos3 = 3-1140\3;

        [combine character sets into partitions]
        partition cytbFull = 2: cytbPos1 cytbPos2, cytbPos3;

        [set parameters of the likelihood model]
        lset applyto=(all) nst=6 rates=invgamma;
                unlink shape=(all) revmat=(all) statefreq=(all) shape=(all)
                pinvar=(all) brlens=(all) topology=(all);

        [set priors on parameters - calculated in jModelTest]
        prset applyto=(all)

        [set parameters of the monte-carlo markov chain]

        [being the search]

        [summarize trees and parameters]
        sumt contype=allcompat;


While this is running think about the following questions:

  1. How many chains are being run?
  2. How many total trees are being sampled?
  3. How many trees are being summarized?
  4. Some people are hesitant to use Bayesian probablilities? What argument(s) could you make against Bayesian phylogenetics.

Open the one of the parameter files in Tracer. Look at the ESSs. They dropped. Why? Did this run reach stationarity? How many more samples would you guess we need? Generations?

How would a real mrbayes run look? I normally run 4-8 runs of 4 chains with 10M generations, sampling every 100-1K. Why would one increase runs over chains or chains over runs?

Since this analsysis will take a while, you can download there results here

wget -O primates_2.5M-100s-2r-4c.bayes.tgz

4) Generating a "publication quailty" tree

Since we don't have time in class to generate a tree using more appropriate parameters, lets use the primates_100K-100s-2r-4c.bayes.con.tre to see how we can take a raw tree and generate a publication quality image.

Open the primates_100K-100s-2r-4c.bayes.con.tre in FigTree.

  • Put the clade probability values (CPVs) on the branches.
  • Color the clades for major primate groups.
  • Order the taxa in a reasonable way.
  • export as an SVG

Open the SVG in Inkscape

  • remove CPVs that fail to meet our significance threshold...which is?
  • Clean up the names for a few of the taxa.
  • Save as an .svg and as a .tiff or .png.
  • These trees are easy to manipulate and can be uploaded straight to the publishers website as a figure. There is no jumping through hurddles trying to meet resolution requirments.

A note about MPI and multithreading.

Normally you could run mrbayes on our cluster using a simple command:


We are using:

mpirun -np 1 mb

This is telling a seperate program mpi to run mb on -np threads. In this case 1. mrbayes can take advantage of mp and run a number of threads equivelant to the number of runs. an example, if you have the following mcmcp command:

mcmcp ngen=1000000 nruns=2 samplefreq=100 printfreq=1000 nchains=10;

then you could distribute your analysis across 20 threads (nruns * nchains = threads).


16 nov 2015

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