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1. (15 minutes) PHYLIP is a collection of programs for phylogenetic analyses written by Joe Felsenstein. The programs are freely available (including source code), and can be used on a variety of different operating system. The programs are modular. Different modules exist to create bootstrap samples, calculate distance matrices and calculate trees from the distance matrices (Fitch and Neighbor), calculate consensus trees, etc.. All programs either use files called infile or intree, or alternatively the user needs to provide the file name. We will use the sequences in testseq1b.txt.

We will use the program protpars as implemented in seaview.

(IGNORE THIS PARAGRAPH, if you are in class) This paragraph has comments on phylip that you can safely ignore for now: If you use protpars directly (without the seqview interface), note that phylip by default treats gaps as a 21st character. If you want to treat the gap as missing data, you need to replace the gap symbol with "?"'s. In case you want to use one of the programs on your own, you need to read the excellent manuals that come with the software.

To calculate a phylogenetic tree from the aligned sequences using protein parsimony, open seaview, and drag the file into alignment window.

Align the sequences using muscle. (click on align, then align all).

In the trees menu select parsimony. Uncheck "ignore all gap sites" and check "gaps as unknown state". To do a more thorough search for the tree that explains the data with the least number of substitutions, select "randomize seq order" 5 times provides a reasonable number of starting points for the heuristic search.

Four notes:

1. Sulfolobus and Thermococcus are Archaea, Borrelia is a Spirochete (bacterium), Acetabularia is a green algae, Daucus is a flowering plant (carrot), Candida, and Saccharomyces are yeasts, Neurospora is another fungus (not a yeast though), Drosophila is an animal (fruit fly) and Trypanosomes are protists.
2. the Borrelia sequence actually is an archaeal type ATPase acquired through gene transfer
3. The sequences in testseq1b.txt (V/A-ATPase catalytic subunits) are quite similar to one another. To test the effect of long branches, I added a homologous, but only distantly related sequence to this file (the ATPase involved in flagellar assembly from Salmonella).
4. Remember that some sequences contain inteins

How are the fungal sequences resolved? (What does this tell us about parsimony and missing data?)
Where does the Salmonella sequence go? (This is as expected, parsimony analyses are very sensitive to the Long Branch Attraction (LBA)).
How many equally parsimonious trees did you obtain?

Perform a distance analysis in Seaview. Select bootstrap, Kimura distances, NJ, and do not check ignore all gap sites. Inspect the bootstrap support values. Does the resulting tree indicate that this phylogenetic analysis was very sensitive to Long Branch Attraction? Which other bipartion would you expect to have high support value? The output of the analysis using seqboot -> protdist -> Neighbor -> consense is here. Does the bipartition table correspond to your expectation?

Seaview also allows calculating trees using the maximum likelihood (ml) principle. The ml tree is the tree under which the sequence alignment has the highest probability. To calculate the ml tree for a sequence alignment, select phyml under trees, and run the program with the default settings.
How does the ml tree compare to the parsimony and neighbor joining trees?

Exercise 2:

Long Branch Attraction (LBA) is a serious problem in phylogenetic reconstruction. LBA denotes the fact that long branches tend to be grouped together with significant support, even though the organisms representing the long branches did not share more recent common ancestry. The support usually is measured through bootstrap support values for the different trees. We have simulated the evolution of 4 sequences (named A,B,C,D) according to the following tree:

Files containing these sequences in multiple sequence fasta format were generated and named according to the length chosen for the two long branches (all scaled in substitutions per site). For the simulation we assumed that the Among Site Rate Variation could be described with a gamma distribution that has a shape factor of 1 (equal to an exponential distribution).

These files are HERE

Your task is to explore the sensitivity of different phylogenetic reconstruction algorithms towards LBA. At the minimum you should use protein parsimony and one protein distance matrix analysis approach. In this case we know that the sequences are aligned as given; however, you to explore the effect that the alignment algorithm has on LBA, we can align them before phylogenetic reconstruction. To keep track of things, name the files accordingly.

NOTE I: If you want to explore the effect of alignment, it might be a good idea to use seaview and muscle as alignment program - especially for the more divergent sequences. We will use the GUI provided in seaview.

Note II: You can divide the labor with your neighbor, distributing different sequences to different students.

We will use programs as implemented in SEAVIEW

3A: To test parsimony, choose the files with x = 0.1, 0.3, 1, 3, 10.

How long are the sequences before and after alignment with muscle?

For the datasets with x = 0.1, 0.3, 1, 3, use the tree menu in seaview, select parsimony, uncheck "ignore all gap sites", check "gaps as unknown states", check "bootstrap with 100 replicates", and move the consensus tree level lever to the left. (Note: If you are interested in the best parsimony tree, then you want to use the original dataset (not bootstrapped) and randomize the input order for several independent heuristic searches, if you do a bootstrap analysis, repeated heuristic searches for each dataset are not worth the time.)

In the following box list the files that you chose, aligned or as provided, the bootstrap support for the correct tree, and the support for the LBA tree:

3B) Explore a distance matrix based approach with respect to LBA (Neighbor joining using Poisson corrected or observed distances work well). Depending on the settings, these might be less sensitive to LBA. x = 0.3, 1, 3, 10 are good choices to explore.

In the following box list the parameters you selected in seaview, the files that you chose (aligned or as provided), and for each file indicate the bootstrap support for the correct tree, and the support for the LBA tree:

3C (optional) Explore the sensitivity of phyml towards LBA. This only works on a fast computer - either transfer the sequences to the cluster, login, qlogin and start the program by typing phyml at the command line, or use seaview on your own computer.

• use the default setting for phyml in seaview (go with nearest neighbor interchange (nni) and the LG substitution matrix).
• the search converges much faster, if you do not align the sequences first)
• use the aLRT (approximate likelihood ratio test) support values (not the real non-parametric bootstrap). The aLRT values are between 0 and 1, with one corresponding to maximum probability for the branch.
• x=1, 3, and 10 are good values to explore.

In the following box list give the parameters you chose for phyml, the files that you chose, indicate if you aligned them or used them as provided, and for each file give the support value for the correct tree, and the support for the LBA tree:

Finished?

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