H4 Following the direction in the attachment below and answer the 3 questions. Price negotiate For this exercise you will use the latest version of MrBaye

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Following the direction in the attachment below and answer the 3 questions.

Price negotiate 

For this exercise you will use the latest version of MrBayes:

Windows: https://github.com/NBISweden/MrBayes/releases/download/v3.2.7/MrBayes-3.2.7-WIN.zip

Mac: https://github.com/NBISweden/MrBayes/releases/download/v3.2.6/MrBayes-3.2.6_MACx64.zip

Mac and PC users should also install the BEAGLE library from https://github.com/beagle-dev/beagle-lib.

Linux: installed using apt (e.g., “sudo apt install mrbayes”)

In addition, you will need an MSA to analyze. Be sure to use the dataset in COURSE RESOURCES as the dataset changes periodically and using one from a previous section of this class will probably not result in you getting the correct answers!

Once you have installed MrBayes, launch it. If you are using a Mac (as I am), use your terminal to navigate to the install directory and type ./mb. You should see the following on your screen:

Text Description automatically generated

Getting Help

The single most important MrBayes command is help. Simply typing help on the command line,

MrBayes > help

will provide a list of the possible MrBayes commands and a brief description of their purpose. If you would like more information on a particular command, including a list of its associated parameters and their current settings, use help <command_name> where <command_name> should be replaced by the name of the command in which you are interested. For example, to get help on the execute command, type:

MrBayes > help execute

Loading and Manipulating Data

The first command that you will need to use MrBayes is execute. This command loads information (either a data matrix or a set of MrBayes commands) from a file. You should take the file you downloaded above (mrbayes.nex) and move it into the MrBayes folder, then type:

MrBayes > execute primates.nex

NOTE: there are many more commands and options available in MrBayes than we will explore in this homework – if you like, feel free to check out the MrBayes Manual online!

Specifying a Model of Sequence Evolution

Basic Model Manipulations

Most model specification is done using the lset command. The most common model specifications performed in lset involve the number of substitution types and the model of rate variation across sites. We’ll use the Kimura 2-parameter model (somewhat outdated, but you are familiar with how it works!):

MrBayes > lset nst=2

Partitioning

It is now widely recognized that the evolutionary process has not been homogeneous across sites. One approach to accommodating heterogeneity in the evolutionary process across sites is to divide sites into distinct subsets (a process called partitioning) and model the evolution of each subset using an independent Markov model of nucleotide substitution.

The first step in performing a partitioned analysis is to define the distinct subsets of your data. These definitions are made using the charset command. The syntax for specifying sites with charset is the same as with the include and exclude commands. For instance, to specify a subset corresponding to the first codon position, type:

MrBayes > charset cod.pos.1 = 1-. 3

Subsets corresponding to codon positions 2 and 3 could be similarly specified as:

MrBayes > charset cod.pos.2 = 2-. 3
MrBayes > charset cod.pos.3 = 3-. 3

Note that each site must be assigned to one and only one subset to perform a partitioned analysis.

Once all of the appropriate character sets have been defined, they need to be explicitly combined into a partition. Appropriately, this is done with a command called partition. For instance, to specify a partitioning scheme with 3 subsets corresponding to the three codon positions, type

MrBayes > partition cod.pos = 3:cod.pos.1,cod.pos.2,cod.pos.3

The number immediately preceding the “:” gives the number of subsets in the partioning scheme.

After defining a partitioning scheme, you need to tell MrBayes that you actually want to use that scheme. This is done with the setcommand. So, to use the “cod.pos” partitioning scheme, type

MrBayes > set partition=cod.pos

Now that the partitioning scheme is all set, you need to define models of evolution for each subset. As above, this is done with the lset command again. The one additional consideration is that you need to tell MrBayes to which subsets you want any particular lset call to apply. To apply one lset command to all subsets within a partitioning scheme, type

MrBayes > lset applyto=(all) nst=2

To make sure the substitution parameters (called revmat by MrBayes) are estimated independently for each partition, you’ll need to unlink the partitions. You’ll have to do the same for the transition/transversion ratio (tratio). Luckily you can do this all at once:

MrBayes > unlink revmat=(all) tratio=(all)

Note: You can also provide the parameters manually rather than allowing MrBayes to calculate them – we’ll let MrBayes do the work this time.

Now let’s start the MCMC (Markov Chain Monte Carlo) process running. Since we’ll be using the default parameters (which you can see if you type help mcmcp), all you have to do is:

MrBayes > mcmc

MrBayes will start computing – depending on your hardware, this may take some time (e.g., 8 minutes on my Mac). If you plan on doing this sort of thing regularly, you should look into the mpi version of MrBayes – it will execute on more cores.

At some point the SD (standard deviation) of the frequencies for each partition will drop below the threshold of 0.01; MrBayes will then ask if you would like to continue. The algorithm may be able to better refine the model if you say YES, but in general this is a diminishing returns thing, and for our purposes you can simply say NO. At this point the computational part of the analysis is done.

OUTPUT ANALYSIS:

In your MrBayes directory you should now have several new files:

mrbayes.nex.run1.p

mrbayes.nex.run1.t

mrbayes.nex.run2.p

mrbayes.nex.run2.t

These correspond to parameter (p) and tree (t) outputs.

Let’s summarize the values:

MrBayes > sump
MrBayes > sumt

You can read the output MrBayes provides regarding the statistics at your leisure; more interesting will be the tree “images” it generates:

A screenshot of a computer Description automatically generated with medium confidence

The first tree is a cladogram with the posterior probabilities for each split.

A picture containing timeline Description automatically generated

The second tree is a phylogram with mean branch lengths represented proportionally (so that the distance from one taxa to another can be seen).

The trees are also printed to a file (mrbayes.nex.con.tre) that can be read by various tree drawing programs to generate a “prettier” output image (say, for publication). If you like you can play around with that. For now, complete the following questions then submit this homework to the class:

1. Paste the images of the trees you generated here (10 pts each)

2. Discuss the Clade Credibility Values you saw in your tree – what do they lead you to believe about your tree? (10 pts)

3. In generating your output, you used the Kimura 2-parameter model. Compare this to the 4 parameter model. (10 pts)

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