Phylogenetics: BayesTraits Lab
|EEB 349: Phylogenetics|
|In this lab you will learn how to use the program BayesTraits, written by Andrew Meade and Mark Pagel. BayesTraits can perform several analyses related to evaluating evolutionary correlation in discrete morphological traits. This program is meant to replace the older programs Discrete and Multistate. You will learn not only how to use the program on the Windows-based PCs in the computer lab, but also how to download and use it on the cluster (the cluster is better for long runs).|
We will use BayesTraits interactively for awhile on the PCs in the computer room (Part 1), then we will set up a non-interactive run on the cluster in Part 2 so that you know how to do this.
- 1 Part 1: Running BayesTraits on your own laptop
- 1.1 Download BayesTraits
- 1.2 Download the tree and data files
- 1.3 Assessing the strength of association between two binary characters
- 2 Part 2: Running BayesTraits on the cluster
Part 1: Running BayesTraits on your own laptop
Download BayesTraits from Mark Pagel's web site, click on the "Software" link, then click on the "Description and Downloads" link under "BayesTraits". Download the version specific to your platform. BayesTraits will unpack itself to a folder containing the program itself along with several tree and data files (e.g. Primates.txt and Primates.trees). I will hereafter refer to the folder containing these files as simply the BayesTraits folder. Go back to Mark Pagel's web site and download the manual for BayesTraits. This is a PDF file and should open in your browser window.
Download the tree and data files
For this exercise, you will use data and trees used in the SIMMAP analyses presented in this paper (you should recognize the names of at least two of the authors of this paper):
Jones C.S., Bakker F.T., Schlichting C.D., Nicotra A.B. 2009. Leaf shape evolution in the South African genus Pelargonium L'Her. (Geraniaceae). Evolution. 63:479–497.
The data and trees were not made available in the online supplementary materials for this paper, but I have obtained permission to use them for this laboratory exercise. The links below are password-protected, so ask us for the username and password before clicking on the links:
- pelly.txt This is the data file. It contains data for two traits (see below) for 154 taxa in the plant genus Pelargonium.
- pelly.tre This is the tree file. It contains 99 trees sampled from an MCMC analysis of DNA sequences.
You should move these files to the aforementioned BayesTraits folder so that they can be easily found by the BayesTraits program.
Assessing the strength of association between two binary characters
The first thing we will do is see if the two characters (leaf dissection and leaf venation) in pelly.txt are evolutionarily correlated.
Trait 1: Leaf dissection
The leaf dissection trait comprises two states (I've merged some states in the original data matrix to produce just 2 states): 0 means leaves are entire (unlobed or shallowly lobed in the original study), and 1 means leaves are dissected (lobed, deeply lobed, or dissected in the original study).
Trait 2: Leaf venation
The leaf venation trait comprises two states: 0 means leaves are pinnately veined (one main vein runs down the long axis of the leaf blade), and 1 means leaves are palmately veined (several major veins meet at the base of the leaf).
To test whether these two traits are correlated, we will estimate the marginal likelihood under two models. The independence model assumes that the two traits are uncorrelated. The dependence model allows the two traits to be correlated in their evolution. The model with the higher marginal likelihood will be the preferred model. You will recall that we discussed both of these models in lecture, and also discussed the harmonic mean method that BayesTraits uses to evaluate models. You may wish to pull up those lectures to help answer the questions that you will encounter momentarily, as well as the BayesTraits manual, which is in the form of a pdf file included with the program.
Maximum Likelihood: Independence model
If you are using Windows, start BayesTraits by opening a console window , navigate to the BayesTraits directory, and type the following to start the program:
BayesTraits pelly.tre pelly.txt
If you are using a Mac, start BayesTraits by opening a terminal window, navigate to the BayesTraits directory, and type the following to start the program:
./BayesTraits pelly.tre pelly.txt
You should see this selection appear:
Please Select the model of evolution to use. 1) MultiState 2) Discrete: Independent 3) Discrete: Dependant 4) Continuous: Random Walk (Model A) 5) Continuous: Directional (Model B) 6) Continuous: Regression 7) Independent contrast
Press the 2 key to select the Independent model. Now you should see these choices appear:
Please Select the analysis method to use. 1) Maximum Likelihood. 2) MCMC
Press the 1 key to select maximum likelihood. Now you should see some output showing the choices you explicitly (or implicitly) made:
Options: Model: Discete Independant Tree File Name: pelly.tre Data File Name: pelly.txt Log File Name: pelly.txt.log.txt Summary: False Seed 3162959925 Analsis Type: Maximum Likelihood ML attempt per tree: 10 Precision: 64 bits Cores: 1 No of Rates: 4 Base frequency (PI's) None Character Symbols: 00,01,10,11 Using a covarion model: False Restrictions: alpha1 None beta1 None alpha2 None beta2 None Tree Information Trees: 99 Taxa: 154 Sites: 1 States: 4
Now type run to perform the analysis, which will consist of estimating the parameters of the independent model on each of the 99 trees contained in the pelly.tre file.
Tree No Lh alpha1 beta1 alpha2 beta2 Root - P(0,0) Root - P(0,1) Root - P(1,0) Root - P(1,1) 1 -157.362972 53.767527 34.523176 35.319157 20.707416 0.249998 0.250002 0.249998 0.250002 2 -158.179984 53.313539 34.182683 36.038859 20.997536 0.249999 0.250001 0.249999 0.250001 . . . 98 -156.647307 52.357626 36.749282 27.270771 13.086248 0.250244 0.249756 0.250244 0.249756 99 -156.532925 52.321467 36.641688 27.402067 13.200124 0.250234 0.249767 0.250233 0.249766
You will notice that BayesTraits created a new file: pelly.txt.log.txt. Rename this file ml-independant.txt so that it will not be overwritten the next time you run BayesTraits.
Try to answer these questions using the output you have generated (ask us if anything doesn't make sense):
- Which occurs at a faster rate: pinnate to palmate, or palmate to pinnate?
- Which occurs at a faster rate: entire to dissected, or dissected to entire?
- What do you think Root - P(1,1) means (i.e. the last column of numbers)?
Maximum Likelihood: Dependence model
Run BayesTraits again, this time typing 3 on the first screen to choose the dependence model and again typing 1 on the second screen to select maximum likelihood. You should see this output showing the options selected:
Options: Model: Discete Dependent Tree File Name: pelly.tre Data File Name: pelly.txt Log File Name: pelly.txt.log.txt Summary: False Seed 3601265953 Analsis Type: Maximum Likelihood ML attempt per tree: 10 Precision: 64 bits Cores: 1 No of Rates: 8 Base frequency (PI's) None Character Symbols: 00,01,10,11 Using a covarion model: False Restrictions: q12 None q13 None q21 None q24 None q31 None q34 None q42 None q43 None Tree Information Trees: 99 Taxa: 154 Sites: 1 States: 4
Here is an example of the output produced after you type run to start the analysis:
Tree No Lh q12 q13 q21 q24 q31 q34 q42 q43 Root - P(0,0) Root - P(0,1) Root - P(1,0) Root - P(1,1) 1 -151.930254 66.451053 37.783888 0.000000 62.220033 23.997490 23.299393 46.110432 36.632979 0.24999 0.249981 0.250026 0.250000 2 -152.925691 67.152271 38.611193 0.000000 60.925185 24.514488 23.937433 45.313366 37.199310 0.24999 0.249983 0.250023 0.250001 . . . 98 -150.816306 36.534843 27.359325 0.000000 66.563262 19.823546 24.944519 63.940577 31.074092 0.250048 0.249750 0.250304 0.249898 99 -150.712705 37.316351 27.260833 0.000000 64.364694 20.107653 25.004246 60.945163 31.658536 0.250030 0.249779 0.250272 0.249919
Before doing anything else, rename the file pelly.txt.log.txt to ml-dependant.txt so that it will not be overwritten the next time you run BayesTraits.
Try to answer these questions using the output you have generated:
- What type of joint evolutionary transitions seem to often have very low rates (look for an abundance of zeros in a column)?
- What type of joint evolutionary transitions seem to often have very high rates (look for columns with rates in the hundreds)?
Bayesian MCMC: Dependence model
Run BayesTraits again, typing 3 on the first screen to choose the dependence model and this time typing 2 on the second screen to select MCMC. You should see this output showing the options selected:
Options: Model: Discete Dependent Tree File Name: pelly.tre Data File Name: pelly.txt Log File Name: pelly.txt.log.txt Summary: False Seed 3792635164 Precision: 64 bits Cores: 1 Analysis Type: MCMC Sample Period: 1000 Iterations: 1010000 Burn in: 10000 MCMC ML Start: False Schedule File: pelly.txt.log.txt.Schedule.txt Rate Dev: AutoTune No of Rates: 8 Base frequency (PI's) None Character Symbols: 00,01,10,11 Using a covarion model: False Restrictions: q12 None q13 None q21 None q24 None q31 None q34 None q42 None q43 None Prior Information: Prior Categories: 100 q12 uniform 0.00 100.00 q13 uniform 0.00 100.00 q21 uniform 0.00 100.00 q24 uniform 0.00 100.00 q31 uniform 0.00 100.00 q34 uniform 0.00 100.00 q42 uniform 0.00 100.00 q43 uniform 0.00 100.00 Tree Information Trees: 99 Taxa: 154 Sites: 1 States: 4
Before typing run type the following command, which tells BayesTraits to change all priors from the default Uniform(0,100) to an Exponential distribution with mean 30:
pa exp 30
Here is an example of the output produced after you type run to start the analysis:
Iteration Lh Harmonic Mean Tree No q12 q13 q21 q24 q31 q34 q42 q43 Root - P(0,0) Root - P(0,1) Root - P(1,0) Root - P(1,1) 11000 -155.195365 -155.195365 78 14.423234 34.800270 8.845985 45.927148 12.622435 50.476188 52.844895 32.149168 0.250068 0.249969 0.249994 0.249968 12000 -154.161705 -154.806538 82 64.601017 12.382781 9.259134 51.796365 12.002095 23.744903 30.316089 21.865930 0.249936 0.249957 0.250095 0.250012 . . . 1009000 -154.343996 -158.180036 30 33.555198 50.086092 11.294490 38.518607 24.461032 47.295157 43.477964 21.726938 0.250057 0.249939 0.250045 0.249959 1010000 -154.195259 -158.179054 87 29.584898 35.410909 2.003582 61.981073 16.976124 14.895266 49.111354 14.419644 0.251115 0.247854 0.252551 0.248480
Before doing anything else, rename the file pelly.txt.log.txt to mcmc-dependant.txt so that it will not be overwritten the next time you run BayesTraits.
A couple of new columns have shown up in the output from the Bayesian analysis that were absent from the ML analysis output. The Harmonic Mean column provides a running estimate of the marginal likelihood. The fact that it is a running estimate means that the estimate is updated using each new sample so that the best estimate of the marginal likelihood from the entire run is provided on the last line of the output.
You will also notice that a column named 'Tree No is present that shows which of the 99 trees in the supplied pelly.tre> treefile was chosen at random to be used for that particular sample point. BayesTraits is trying to mimic sampling trees from the posterior distribution here; it cannot actually sample trees from the posterior because we have given it only data for two morphological characters, which would not provide nearly enough information to estimate the phylogeny for 154 taxa.
Try to answer these questions using the output you have generated:
- What is the estimated log marginal likelihood for this analysis?
- Based on the Bayesian Model Selection lecture, do you expect this to be an accurate estimate of the true log marginal likelihood? If not, is it an over- or and under-estimate?
Run BayesTraits again, this time specifying the Independent model, and again using MCMC and pa exp 30. Rename the output file from pelly.txt.log.txt to mcmc-independant.txt.
- What is the estimated log marginal likelihood for this analysis?
- Which is the better model according to these estimates of marginal likelihood?
Run BayesTraits again, specifying Dependent model, MCMC and, this time, specify the reversible-jump approach using the command rj exp 30. Type run to start, then when it finishes rename the output file rjmcmc-dependent.txt.
The reversible-jump approach carries out an MCMC analysis in which the number of model parameters (the dimension of the model) potentially changes from one iteration to the next. The full model allows each of the 8 rate parameters to be estimated separately, while other models restrict the values of some rate parameters to equal the values of other rate parameters. The output contains a column titled Model string that summarizes the model in a string of 8 symbols corresponding to the 8 rate parameters q12, q13, q21, q24, q31, q34, q42, and q43. For example, the model string "'1 0 Z 0 1 1 0 2" sets q21 to zero (Z), q13=q24=q42 (parameter group 0), q12=q31=q34 (parameter group 1), and q43 has its own non-zero value distinct from parameter groups 0 and 1.
You could copy the "spreadsheet" part of the output file into Excel and sort by the model string column, but let's instead use Python to summarize the output file. Download the file btsummary.py file and run it as follows:
This should produce counts of model strings. (If it doesn't, check to make sure your output file is named rjmcmc-dependent.txt because btsummary.py tries to open a file by that name.) Answer the following questions using the counts provided by btsummary.py.
- Which model string is most common?
- What does this model imply?
Notice that many (but not all) model strings have Z for q21. One way to estimate the marginal posterior probability of the hypothesis that q21=0 is to sum the counts for all model strings that have Z in that third position corresponding to q21. It is easy to modify btsummary.py to do this for us: open btsummary.py and locate the line containing the regular expression search that pulls out all the model strings from the BayesTrait output file:
model_list = re.findall("'[Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9]", stuff, re.M | re.S)
The re.findall function performs a regular expression search of the text stored in the variable stuff looking for strings that have a series of 8 space-separated characters, each of which is either the character Z or a digit between 0 and 9 (inclusive). Copy this line, then comment out one copy by starting the line with the hash (#) character. Now modify the other copy by forcing the regular expression search to look only for model strings having a Z in the third position:
#model_list = re.findall("'[Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9]", stuff, re.M | re.S) model_list = re.findall("'[Z0-9] [Z0-9] [Z] [Z0-9] [Z0-9] [Z0-9] [Z0-9] [Z0-9]", stuff, re.M | re.S)
Rerun btsummary.py, and now the total matches should equal the number of model strings sampled in which q21=0.
- So what is the estimated marginal posterior probability that q21=0?
- Why is the term marginal appropriate here (as in marginal posterior probability)?
Part 2: Running BayesTraits on the cluster
We will now switch to using the (older, Mac-based) cluster to run BayesTraits. BayesTraits is not installed on the cluster, so this provides a good opportunity to show you how to download and unpack software into your home directory and run programs not already installed for you.
BayesTraits is different than most of the programs used on the cluster in that it is interactive: it expects you to type some commands after it starts. This is difficult when you use qsub to submit a job to the cluster since you are not there to supply these commands once it starts. This part of the lab shows you how to get around this difficulty when using programs like BayesTraits.
Downloading and unpacking BayesTraits
First, connect to bbcxsrv1.biotech.uconn.edu to get a command prompt (using either putty.exe on Windows or the ssh command in your Mac Terminal application). The full URL to the OS X PPC version of BayesTraits is
If you had a browser open, and you typed in this URL, your browser would save the file BayesTraits-OSX-PPC-V1.0.tar.gz on your hard drive. But you are not using a browser on the cluster, you are logged in using secure shell.
You could download the file to your PC, then upload it to the cluster using FileZilla (Windows) or Fugu (Mac), but let's instead use the curl command:
curl http://www.evolution.reading.ac.uk/Files/BayesTraitsV2-Beta-Linux64.tar.gz > BayesTraitsV2-Beta-Linux64.tar.gz
This tells curl to access the specified URL (curl will stand in for a web browser) and the -o option says to save the resulting file as BayesTraits-OSX-PPC-V1.0.tar.gz.
Once you have the file in your home directory (use the ls command to check), you will need to unpack it using the tar command:
tar zxvf BayesTraits-OSX-PPC-V1.0.tar.gz
The file extension .tar.gz is very common for software targeting Linux and Mac OS X systems. This extension has a very specific meaning: the .tar part means that the file is actually an archive comprising several files saved one after the other (tar stands for "tape archive", even though no one uses magnetic tapes anymore). The .gz part means that the archive has been compressed using the gzip program. The command line switches for tar (zxvf) are decoded as follows:
- z means unZip the archive first
- x means eXtract the component files
- v means be Verbose and tell me what's going on as you work
- f simply means that the name of the File to extract follows immediately afterwards
Once the tar command has completed, you should have a directory named BayesTraits. Use the cd command to move into that directory, then use the ls command to see what's there. You should see the same 5 files as before.
We will do a very simple analysis just so you will know how to run BayesTraits in batch mode. Because analyses on the cluster are submitted via the qsub command, and thus no one will be present to answer those questions BayesTraits asks when it runs, we'll supply the answers in a file (arbitrarily named commands.txt) and feed that file to BayesTraits when it is invoked.
Create the commands.txt file
Create a file inside the BayesTraits directory using the pico editor named commands.txt, and save the following in the file:
# choose the model (1 = multistate, 2 = independence, 3 = dependence) 3 # choose the method (1 = ML, 2 = MCMC) 2 ratedev 10 rjhp exp 0 30 run
The lines starting with a pound sign (or hash) are comments and are optional. Otherwise, this file just presents to BayesTraits exactly what you would type if you were running the program interactively.
Create the qsub script
As you know by now, you use the qsub command to submit a job to the cluster. The advantage of using the qsub command is that it will start your job on a processor that is currently idle (assuming there are idle processors available in the cluster). Not using qsub results in your job being started on the head node of the cluster. The head node is the processor that everyone uses to interact with the cluster, and if you start a run there, it has a fair chance of simply being killed as soon as the system administrator (Jeff Lary) notices it because it makes the head node appear very sluggish to other users!
The qsub command accepts the name of a script file that carries out your wishes, so we must first create this script (use pico to create and save the following in your home directory as a file named btgo, for example):
#$ -o junk.txt -j y #$ -m bea #$ -M firstname.lastname@example.org cd $HOME/BayesTraits ./BayesTraits Primates.trees Primates.txt < commands.txt
You have experienced these scripts before. The first few lines begin with the sequence of characters #$, which alerts qsub that a qsub command is coming. The first command is the -o junk.txt -j y part, which tells qsub that you want the output of the run saved in a file named junk.txt and you would like any error message appended to this same file (the -j y part is indeed cryptic, but stands for "join [standard error to output] yes"). Be sure to delete or rename any file named junk.txt that already exists.
The second line says to send an email notification before, at the end of, and in case of abort.
The third line tells the Sun Grid Engine where to send the email notifications. You should of course change this to your own UConn email address Email addressed outside UConn will be ignored.
The fourth line says to cd into the BayesTraits directory (this means that the BayesTraits folder will now be the working directory, and any output files created by the program will be saved there).
The fifth line actually starts the program. Since we are running this on the cluster and are not so limited by time, we'll use the full Primates.trees file (containing 500 trees). Note the very important commands.txt part, which feeds the answers to all questions to the BayesTraits executable. Also, note the period-slash (./) before the name of the program. This says that the program can be found in the current directory (the current directory is signified by a dot; one of the crazy things about unix is that the current directory is not searched by default when looking for files!).
Submit the script
Now submit the job as follows:
As before, you can periodically check to see if it is still running using the qstat command. You can use pico to look at the junk.txt file in your home directory, or look in the BayesTraits directory and examine the files created there as the program runs. A useful command is tail:
cd $HOME/BayesTraits tail Primates.txt.log.txt
This shows you only the last few lines of the file listed. To show the last 100 lines, you can use the -n switch:
tail -n 100 Primates.txt.log.txt