Tutorial: Phylogenetic Analysis on BioHealthBase Written by: Catherine A. Macken Version 1: February 2009

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1 Tutorial: Phylogenetic Analysis on BioHealthBase Written by: Catherine A. Macken Version 1: February 2009 BioHealthBase provides multiple functions for inferring phylogenetic trees, through the Phylogenetic Tree page, which is linked from the left-hand navigation bar. This document is intended to help you to use this page. Contents: 1. Getting data into the tree page 1.1) Upload a file from your desktop 1.2) Paste a file into the text box 1.3) Generate a tree from a working set. 2) Which evolutionary model for your tree inference? 2.1) Run ModelCompare 2.2) Choose a model of evolution 3) Controlling the look of your tree 4) Other details 1

2 Fig. 1: Phylogenetic Inference page: references to sections of this document in red. (1.1, 1.2, 1.3) (1.1) (1.2, 1.3) (2.1, 2.2) (3) (2.2) 2

3 1) Getting data into the tree page 1.1) Upload a file from your desktop This file can be aligned, or unaligned or in Phylip format. You must specify which format you are using by selecting a radio button at the top of the page. If you enter unaligned data, MUSCLE will be used to align sequences before running the tree programs. You should use nucleotide sequences for flu trees, unless you are including widely divergent sequences, such as from different serotypes of flu A or different flu types for internal genes. 1.2) Paste a file into the paste box The constraints for uploading a file (1.1) apply. 1.3) Generate a tree from a working set. Use the View Tree button above the listing of your working set (see Fig. 2 below). This automatically pastes your sequences into the text box so that you can proceed as in (1.2) above. Figure 1 shows the result of choosing the View Tree button from a display of the working set below. Fig. 2: Selecting tree building as an action to carry out on your working set. (1.3) 3

4 2) Specifying the evolutionary model for your tree inference There are many theoretical approaches to inferring a phylogeny. BioHealthBase takes an approach that uses maximum likelihood to measure the quality of an inferred tree. Maximum likelihood estimation (MLE) is generally regarded as leading to a high quality tree, one that will stand up to the scrutiny of reviewers in most situations. However, MLE requires that you use a model for the evolution of the sequences that will be in your tree. There are many frequently used models of evolution available. From previous experience, you may know which one you want to use. In this case, you will want to select the radio button for Choose a model of evolution. Then go to (2.2) for further instructions. If you do not know which model to use, then select the radio button for Run ModelCompare to suggest the best model of evolution to get guidance on the choice of model for your data set. Then go to (2.1) below for further instructions. 2.1) Run ModelCompare ModelCompare first makes a quick estimate of a tree topology for your data. This will be a reasonable, but probably not optimal, estimate. It then keeps this topology fixed while it optimizes the estimates of the parameters of the models. In this way, multiple models can be compared in a modest amount of time. (Optimization of both the topology and the parameters is too time-consuming to carry out for each of the six models.) You can confidently take the outcome from ModelCompare to guide your choice of a model of evolution to do the full optimization of estimates of the topology and parameters. ModelCompare has some of the flavour of ModelTest, which requires access to PAUP* (Posada D and Crandall KA (1998): Modeltest: testing the model of DNA substitution. Bioinformatics 14 (9): ). However, ModelCompare is stand-alone, compares a smaller range of models, runs faster, focuses on maximum likelihood optimization and is web-driven. Six different models are tested on your data set. These models differ in the number of parameters used, and hence in their power for fitting to data. The more parameters used, the better the fit to data expected. The models tested are HKY, TN93 and GTR, with or without the additional option of allowing for variable rates of evolution at the different positions in the sequence. ModelCompare returns a list of log-likelihood values. Roughly speaking, the higher (less negative) the log-likelihood value, the better is the fit of the model to the data. ModeCompare only gives guidance on the choice of the best model. It does NOT fit the best model. 4

5 Fig. 3: Results from running ModelCompare In this example, the log-likelihood for TN93plus is 1.1 greater than the loglikelihood for HKYplus. This makes it look like TN93plus is a better model than HKYplus. But TN93plus uses one more parameter. Basically, you want to see that each parameter added leads to an increase of 2.0 or more in the log-likelihood value before you say that the model with more parameters is better. Therefore, there is really no difference in the fit of these two models. (See Inferring Phylogenies, by J. Felsenstein (2004) for more details on interpreting loglikelihood values.) GTRplus is the best model for these data, since it has the largest log-likelihood value. However, after adjusting for the number of parameters used, it is not much better than HKYplus. HKYplus is substantially better than HKY. HKY will run faster than any of the other models. HKYplus will run much faster than GTRplus. If you are building a tree for a large data set, you may wish to use HKYplus, as a compromise between accuracy and speed. If you have a small data set, then you might choose GTRplus. Once you have used ModelCompare to suggest a model of evolution, you should go back to the phylogenetic tree page and run the option: Choose a model of evolution. Alternatively (see Fig. 3), you can select a radio button for your preferred model and click on the Select button to automatically set up the next steps for you. 2.2) Choose a model of evolution When you choose this option, you will get a fully optimized phylogeny for your data. You are given the option of implementing any one of 12 different models of evolution. Six of these are the same as those tested in ModelCompare. The plus gamma option allows for sites in the sequence to evolve at different rates. (See Inferring Phylogenies, by J. Felsenstein (2004) for more details on models of evolution.) Depending on which model you choose, additional text boxes may open for input of parameter estimates. You may know estimates from a previous analysis, in which case you can enter them here and your tree will come back much more quickly. If you do not know values, then leave the text box blank and the parameter will be estimated. It is NOT a good idea to enter parameter estimates from ModelCompare. These were obtained without optimizing the tree topology and hence will not be optimized. 5

6 If you use the plus gamma option, you MUST enter a value for number of categories. Basically, you are being asked: if you classify the rates of evolution at the different sites, how many classes might be reasonable? For example, you might choose 3 rates: slow, medium and fast. Three is generally not a good number of categories, because the fast rates tend, in fact, to be fast, very fast, and extremely fast. Specifying five categories tends to work well. 6

7 3) Controlling the look of your tree It is often helpful to designate a particular virus as a reference point in your tree. This reference point is called an outgroup. You can tell the tree builder what record in your data set should act as the outgroup. Follow the instructions above the text box for correct specification. 4) Other details Because phylogenetic inference can be time-consuming, ModelCompare and BuildTree jobs are run on a ticket system. Once you start your job running, you will be presented with the number of your ticket. If you are patient, or have a very small tree (20 sequences runs in about 2 minutes to Build Tree), you can sit and watch the screen until your results are returned. If you would rather do something more interesting while the server is working, you can retrieve your results at a later time, by clicking on the links Retrieve ModelCompare or Retrieve Tree in the left-hand navigation bar. You can view your tree in an ATV applet (which will automatically download onto your desktop), or download the tree in a visual format (pdf file) or as a text file of the Newick representation of your tree. The last format is a standard form for importing a tree into other display applications. 7