author	level	title	subtitle	beastversion
Nicola F. Müller	Intermediate	Estimating Recombination Networks with the Recombination Package	Estimating Recombination Networks	2.6.3

Background

Phylogenetic trees are often used to describe the history of genetic sequences. This relatedness of genetic sequences can be used to learn how, for example, pathogens isolated from different individuals are related, which tells us something about the transmission history. Recombination breaks the central assumption that this relatedness can be represented by a tree and requires it to be denoted by a network instead.

The coalescent with recombination models a joint coalescent and recombination process {% cite muller2020bayesian --file Reassortment-Tutorial/master-refs %}. To do so, it models how network lineages coalesce and recombine backwards in time. This is the backwards in time equivalent of the template switching model of recombination upon coninfection, where one such event occurs per coinfection event.

In order to perform inference under the coalescent with recombination, the Recombination package uses MCMC sampling of recombination networks and how local trees (i.e. trees at individual nucleotide positions) are embedded within those networks. The way the Recombination package is implemented allows the user to follow the usual setup for BEAST2 xml files in BEAUti.

After running a coalescent with recombination analysis, the post-processing works slightly different to other models. Since we have to analyse a network and not just a tree, the Recombination package implements a BEAST2 app that summarizes networks and works similar to treeAnnotator.

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Programs used in this Exercise

BEAST2 - Bayesian Evolutionary Analysis Sampling Trees 2

BEAST2 (http://www.beast2.org) is a free software package for Bayesian evolutionary analysis of molecular sequences using MCMC and is strictly oriented toward inference using rooted, time-measured phylogenetic trees. This tutorial is written for BEAST v{{ page.beastversion }} {% cite BEAST2book2014 --file Reassortment-Tutorial/master-refs %}.

BEAUti2 - Bayesian Evolutionary Analysis Utility

BEAUti2 is a graphical user interface tool for generating BEAST2 XML configuration files.

Both BEAST2 and BEAUti2 are Java programs, which means that the exact same code runs on all platforms. For us it simply means that the interface will be the same on all platforms. The screenshots used in this tutorial are taken on a Mac OS X computer; however, both programs will have the same layout and functionality on both Windows and Linux. BEAUti2 is provided as a part of the BEAST2 package so you do not need to install it separately.

IcyTree.org

IcyTree.org is a web-based tree viewer that, in addition to trees, also allows the user to visualize networks in the extended newick format.

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Practical: Setting up a coalescent with recombination analysis

The coalescent with recombination is an approach that allows inferring recombination networks and recombination rates from multiple sequence alignments. It therby assumes that the recombination process can be modeled such that everything on one side of a breakpoint originates from one parent lineage and everything else from the other parent lineage.

In this tutorial, we will learn how to create an xml for a coalescent with recombination analysis, how to then run this xml, as well as how to process and visualize the output of such an analysis.

The Data

The data consists of sequences of the nucleocapsid protein of one of the seasonal human coronaviruses called 229E. The nucleocapsid protein is a structural protein of coronaviruses.

Overall, the dataset we use in this tutorial consists of 25 sequences of the nucleocapsid protein of 229E viruses sampled over several years https://www.viprbrc.org/. The sequences are already aligned and can be found in the data folder.

To access the data on your computer, clone the Github repository for this tutorial to your computer using the command: git clone https://github.com/nicfel/Recombination-Tutorial. You will use the 229e_nucleocapsid.fasta in the data/ directory for this tutorial.

Download the Recombination package

First, we have to download the Recombination package using the BEAUti package manager. To do so, open BEAUTi and then go to File >> Manage Packages and click on Recombination and then the Install/Upgrade button.

Figure 1: Download the Recombination package

After the package is installed, re-start BEAUti.

Load in the sequences

In order to load in the sequences into BEAUti, drag and drop the 229e_nucleocapsid.fasta file into the partitions window. Then a window will pop up, where we'll have to specify that all the sequence files just loaded in are nucleotide sequences. This is so BEAUti knows which site models to suggest.

Figure 2: Drag and drop the sequence files into the partitions window.

While often done for such analysis, the Recombination package can at the moment not account for rate variations across different sites. We have therefore nothing left to do in this panel and can move on to setting the sampling times.

Setting up the sampling times

Next, we'll have to set up the sampling times. To do so, go to Tip Dates and select Use tip dates Specify the dates format to be yyyy-M-dd and press the Auto-configure button. In the window that should pop up, select use everything and choose "after last" from the drop-down menu. Then enter the character "/".

Figure 3: Split character and take the last group to get the sampling times .

Setting up the site model

As a site model, we will use an $HKY+\Gamma_4$ model. To do so, first set the site model from JC69 to HKY, which allows transition and transversion rates to differ. Next, set the Gamma Category Count to 4. Setting the Gamma Category Count to 4 uses a discretized version of a gamma distribution to model different rate categories across sites. 4 or 5 different categories has been shown to be a good compromise between computational efficiency and still being able to approximate the gamma distribution well enough in practice.

Figure 4: Setting up the site models tp an $HKY+\Gamma_4$ model.

Setting up the Priors

We can leave the clock model as is, which means that we use a Strict Clock Model (default) and can directly go the the Priors tab. The first and most important thing we have to do here, is to to change the Yule Model to Coalescent With Recombination Constant Population.

Figure 5 Changing the Yule model to the coalescent with recombination model.

We next have to set the prior distribution on the parameters. The prior distribution on the effective population size can be left as is, but we have to change the prior on the recombination rate. In the drop-down menu, set the prior distribution on the recombinationRate to be an exponential distribution. Then click the arrow to the left of recombinationRate to enter a mean of 0.0001. This means that we assume a priori that on average that one recombination event occurs between every pair of adjecent positions in the alignment every 1000 years. Since our alignment has 1170 nucleotides, we expect one event to occur on a lineage every 1000/(1170-1) years.

Figure 6: Setting the prior distribution on the recombination rate.

Setting up the Chain Length

Next, we can go to the MCMC panel to set the chain length. For this analysis, which doesn't describe a very complex recombination network, a chain length of 10 million should be enough. This was the last step of setting up the xml and we can now save it by going to File > Save as and giving this configuration file a name like 229e_N_recombination.xml.

Figure 7: Save xml.

Run the xml

Next, open BEAST and run the xml. This should take somewhere in the order of 40 to 60 minutes. Alternatively, the folder precooked_runs contains the log files of the run.

Inspect the run in Tracer

Next, we have to check whether everything converged. To do so, we can open the program Tracer and load the *.log file. All ESS values should optimally be above 200.

Figure 8: Check convergence in Tracer.

Next, we can check what rates were inferred. The clockRate denotes the average rate of evolution in 229E nucleocapsid. The recombinationRate denotes the rate of recombination (from present to past) per lineage and year between adjecent positions in the genome.

Summarize the posterior distribution of networks

Next, we can summarize the distribution of networks by maximizing the clade credibilities. To do so, open BEAUti and select File > Launch Apps. Click Launch. Then, select Recombination Network Annotator.

Next, choose the 229e_nucelocapsid.networks.trees file as input for the Recombination Network log file and choose the file where the mcc network should be saved to and press analyse.

Figure 9: Produce the maximum clade credibility network.

Visualize the network using icytree.org

Next, open your browser and go to the webpage icytree.org{% cite vaughan2017icytree --file Reassortment-Tutorial/master-refs %} The resulting MCC network file can now be dragged and dropped into icytree to visualize the network. Icytree plots the network as a base tree that is connected by dotted branches. This implies that, at a recombination event, there is a difference between the two parent branches. This is, however, not the case in this coalescent with recombination model, but for simplicity is plotted like this. The "main" branch here is the always the parent branch that carries more genetic material, in the sense that more of its genetic material is sampled in the future and therefore shows up the network.

Figure 10: Visualize the mcc network in icytree.

The 95 $%$ highest posterior density intervals for node heights can be plotted by going to Style > Node height error bars. The posterior support for each node can be shown by going to Style > Internal node text.

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Useful Links

• BEAST 2 website and documentation: http://www.beast2.org/
• Join the BEAST user discussion: http://groups.google.com/group/beast-users

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Relevant References

{% bibliography --cited --file Reassortment-Tutorial/master-refs %}