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author bcclaywell
date Mon, 12 Oct 2015 17:57:38 -0400
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1 <tool id="ARGO_NAVIS" name="Introduction" version="1.0.0">
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2 <description>to Argo Navis</description>
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3 <help>
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4
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5 **NOTE**: This "tool" is documentation only; the "Execute" button above does nothing.
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7
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8 Argo Navis
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9 ----------
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10
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11
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12 **Argo Navis** is a set of Galaxy tools to carry out Bayesian discrete phylogenetic trait analyses using `BEAST2`_ to sample posteriors and `PACT`_ to extract information from these posteriors.
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13 We recommend that you read this entire introduction, and follow through the tutorial below for an overview of how Argo Navis works and how to use it.
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14 From there, you can follow up with documentation in the individual tools for more detailed information and instruction as needed.
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16
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17 About discrete traits
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18 ---------------------
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19
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20 In general, a *discrete trait* is one which takes on some finite number of values.
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21 In a phylogenetic setting, a discrete trait can represent geographical location, host species of a virus, or tissue compartment of a host.
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22 We can model evolutionary histories in such a way that transitions in this discrete trait are accounted for.
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23 These transitions might represent physical migration of organisms, transmission of a virus from one host species to another,
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24 or tropism of viruses between different host tissues.
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25 Modeling these states and their transitions produces information relevant to the biological characteristics they represent.
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26
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27 Through the course of this documentation, we'll frequently refer to the discrete traits as *demes*.
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28 The word *deme* comes from population genetics (as in the root of *demographics*), where much of the foundational theory for these analyses was developed.
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29 However, this is for convenience, and does not preclude usage of this tool for other use cases.
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31
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32 How Argo Navis is set up
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33 ------------------------
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34
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35 Argo Navis uses BEAST to perform phylogenetic and discrete trait analyses.
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36 While BEAST is a powerful tool, it can be daunting to set up for beginners.
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37 Argo Navis automates much of this setup, requiring you only to specify your sequence and discrete trait data.
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38 It also exposes the options most pertinent to ancestral discrete trait analyses in a friendly manner.
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39
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40 The second component of Argo Navis uses PACT (Posterior Analysis of Coalescent Trees), developed by Trevor Bedford.
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41 PACT takes the labeled tree output file from BEAST, and performs highly customizable analyses on it.
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42 While, like BEAST, PACT can be daunting to set up and run for beginners, Argo Navis wraps the features you'd most commonly use into a simple interface.
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44
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45 BEAST takes a long time to run
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46 ------------------------------
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47
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48 In case you're not already aware, BEAST can take quite a while to run for a real analysis.
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49 Analyses frequently take hours, days, even weeks or months, depending on the amount of data and complexity of the analysis.
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50 However, when you first run the Argo Navis BEAST tool, you'll notice that it runs in a few minutes on most datasets.
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51 These default settings are intended for quickly running your data, to make sure things are set up properly.
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52
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53 Once you've seen your data from one end of the tool to the other, *you will likely need to greatly increase the sampling interval.*
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54 Then once your first real run has completed, you'll need to check that it ran long enough.
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55 If it did not, you can perform a Resume run, to pick up where you left off.
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56 All this functionality is covered below, and in detail in the documentation residing within the BEAST tool.
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57
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58
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59 A brief tutorial
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60 ----------------
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61
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62 The following is a brief walk through of how to use Argo Navis.
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63
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64 First download the `sample data`_ you'll be using with this tutorial.
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65 After unzipping the contents, you'll find three files: an alignment FASTA file, a metadata CSV file, and a color specification CSV file.
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66 Before moving forward, it's worth opening these up in a text file and/or Excel to get a sense for what's in each.
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67
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68 **Loading Data**
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69
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70 First lets load this data into the Galaxy tool.
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71 You can do this by following ``Get Data > Upload File`` in the Tools menu on the left hand side of this page.
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72 This will bring up a box into which you can drag the files you downloaded.
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73 When you do this, you should notice the file names show up in the box.
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74
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75 Next specify the file type for each of these files (unfortunately, the "Auto-detect" setting doesn't really work).
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76 The alignment file should be of type "fasta", and the other two files of type "csv".
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77 Once you've done this, click on the "Start" button of the mini-window.
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78 When the "Status" indicator reaches 100%, you can click "Close".
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79 Note that our files should now show up on the right side of the page, in the "History" panel.
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80
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81 **Running BEAST**
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82
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83 Now that we've loaded the data, we can start a BEAST run.
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84 Click "Run BEAST" in the Tool menu (just below where you clicked to get to this introduction) to open the tool.
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85
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86 First, select the alignment file we just uploaded from the "Sequence alignment" file selector.
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87 Next, we'll specify our deme information using the metadata file we uploaded (you can also use regular expressions to parse
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88 this information out of the alignment file if you would rather).
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89 Click on the "Metadata file" selector, and point it to the metadata.csv.
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90 In our data, the "Deme column" is not named "deme" (the default), but "community".
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91 So in "Deme column" enter "community", so Argo Navis gets the right data.
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92
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93 Now, we also happen to have sampling date information in our metadata file.
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94 We can tell Argo Navis to use this by clicking "Enable" next to "Date column", which will allow us to enter in
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95 "date_collected" for this value, to match that in the date column name in our metadata file.
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96
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97 So the run goes quickly, decrease the sampling interval to "10".
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98 There are a number of other options we've ignored, but for now, let's keep it simple.
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99
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100 Now that everything is set up, click the "Execute" button.
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101 You should now see some new files show up in the history, with spinners indicating that the run is still converging.
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102
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103 **Checking Convergence**
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104
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105 Once the new files turn green and the spinners stop, our run is complete.
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106 The very *first* thing we should do is look at the effective sample size (ESS) analysis of the run.
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107
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108 Find the "BEAST effective sample size stats" output file, and click on the little eyeball icon associated with it (View Data).
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109 You should see a report with "Recommendation: Should run longer", since we didn't run BEAST for very long.
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110 Additionally, you should see a complete list of the ESS values of all the parameters in our model.
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111
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112 **Running BEAST longer**
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113
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114 Clearly we need to run BEAST longer.
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115 Go to "Run BEAST" in our Tool menu, then at the bottom select "Yes, please" for the "Resume from a previous run?" option.
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116 Once you select this, a number of additional option will open up.
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117 You must specify the "BEAST logfile", "BEAST treefile", and "BEAST state file" from your previous run for each of
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118 the respective inputs which appeared.
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119 Additionally, you will have to select "Custom or Resume" for "BEAST file specification" just above, and
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120 specify the "BEAST config file" output by the last run of the tool.
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121
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122 The last thing we'll need to do is decide how many new samples to draw.
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123 Let's set this value to 50,000, for 5x the number in our initial run.
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124 Once you've done this, Execute the tool once more.
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125 (Note that all of the other input parameters are useless when you do a Resume run, as the data in the BEAST config file will be used, and must be the same as the run being resumed).
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126
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127 Once the run is complete, check the convergence as you did for the initial run, and note that the ESS values have improved.
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128 It probably *still* says that we need to run for longer, but this is okay now for illustrative purposes.
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129
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130 Next take a look at the other output files we have.
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131 In addition to the files we had before, we also now have "Trimmed" versions of our logfile and treefile.
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132 Because Resume runs add to existing logfiles and treefiles, and must do so using the same sampling interval as we used for the initial (in this case much shorter) run, the complete versions of these files can be quite large, making further analysis more difficult.
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133 For this reason, trimmed versions of the files are produced.
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134 The *un*-trimmed versions should *only* be used for follow up Resume runs.
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135 The *trimmed* versions should be used for everything else.
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136
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137 **Tracer**
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138
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139 Once your run has passed the ESS tests (we're going to pretend ours have), it's a good idea to also run it through `Tracer`_.
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140 Tracer is a program for analyzing the logfiles produced by BEAST.
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141 It lets you directly analyze the shape of the parameter states explored, which is a good idea, even if the ESS values check out.
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142 It is also capable of a number of powerful analyses, such as testing for covariance between parameters of your model.
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143 For more information, see the `BEAST tutorial on Tracer`_.
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144
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145 **PACT**
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146
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147 Once your BEAST output has checked out via ESS and been run through Tracer, it's time to run PACT!
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148 First click on "Run PACT" in the "Tools" menu.
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149 The only *required* input is "BEAST treefile".
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150 However, as mentioned above, if you did a Resume run, you should use the "Trimmed BEAST treefile" output from yhour last run
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151 as the input for the PACT tool
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152 For this run, we'll leave all the other settings to their defaults.
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153 When you're ready, click "Execute".
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154
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155 Once the runs have completed, click on the eye icon next to the "PACT figures" output.
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156 This will bring up the main results of the analysis: the maximum likelihood ancestral history, a skyline proportions histogram,
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157 and a migration rate plot.
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158 These outputs are all described in detail in the PACT tool's documentation.
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159
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160 Where to go from here
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161 ---------------------
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162
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163 Start running your own data!
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164 Read some of the `BEAST tutorials`_, check out the `BEAST2 website`_, and look at the `Ancestral State Reconstruction tutorial`_
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165 upon which this tool is loosely based.
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166
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167 It's also worth taking a look at the published literature showcasing applications of these methods:
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168
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169 Bedford T, Cobey S, Beerli P, Pascual M. (2010) `Global migration dynamics underlie evolution and persistence of human influenza A (H3N2)`_. PLoS Pathog 6: e1000918.
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170
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171 Hopefully, we'll be able to grow this list over time.
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172
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173 .. _sample data: https://github.com/fredhutchio/argo-navis/raw/master/data/example_data.zip
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174 .. _Tracer: http://tree.bio.ed.ac.uk/software/tracer/
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175 .. _BEAST tutorial on Tracer: http://beast.bio.ed.ac.uk/analysing-beast-output
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176 .. _Ancestral State Reconstruction tutorial: http://beast-classic.googlecode.com/files/ARv2.0.1.pdf
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177 .. _BEAST tutorials: http://beast.bio.ed.ac.uk/tutorials
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178 .. _BEAST2 website: http://beast2.org/
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179 .. _BEAST2: http://beast2.org/
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180 .. _Global migration dynamics underlie evolution and persistence of human influenza A (H3N2): http://bedford.io/papers/bedford-global-migration/
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181 .. _PACT: http://bedford.io/projects/PACT/
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182
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183 </help>
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184 </tool>