Viral Phylogenetics

In this activity, you will perform a viral phylogenetics analysis on real viral genome sequences. Specifically, in the datasets folder, we have provided whole genome sequence datasets for two viruses of interest: HIV-1 and SARS-CoV-2. Each dataset contains 10 genome sequences collected from real people, found in the _sequences.fas file, as well as each sequence's collection date (i.e., the date on which the viral sample was collected from the patient), found in the _dates.txt file.

We will be performing our viral phylogenetics analyses using ViralWasm-Epi, a web-based tool for performing viral molecular epidemiological analyses:

https://niema-lab.github.io/ViralWasm-Epi

Step 1: Multiple Sequence Alignment (MSA)

Much like how human genomes "evolve" (i.e., mutate) over time (e.g. between parent and offspring), viral genomes similarly mutate as the viruses replicate. Recall that the most common types of mutations are substitutions (in which one nucleotide is replaced with another), insertions (in which one or more nucleotides are inserted into the genome), and deletions (in which one or more nucleotides are deleted from the genome). Also, recall that these mutations are heritable (i.e., they pass down from parent to offspring).

If we were to sequence multiple viral genomes that descended from a common ancestor, any substitutions that occurred between the common ancestor and the present-day sequences would still result in sequences that line up with the ancestral sequence (and with each other). For example, imagine an ancestral viral genome sequence is ACGTAC, and imagine the first A mutated into a G, resulting in a descendant viral genome sequence of GCGTAC. The two genome sequences still line up properly (i.e., if I write them on top of one another, each column of the sequences corresponds to a single letter in the ancestral sequence):

123456 # position in ancestral sequence
ACGTAC # ancestral sequence
GCGTAC # descendant sequence
123456 # position in ancestral sequence

However, any insertions or deletions (commonly lumped together to create the colloquial term indels) would shift parts of the descendant sequence to the left (deletion) or right (insertion), breaking how the sequences line up. For example, imagine that the first A actually got deleted:

123456 # position in ancestral sequence
ACGTAC # ancestral sequence
CGTAC  # descendant sequence
23456  # position in ancestral sequence

Simiarly, if a C was inserted at the beginning of the sequence instead, the sequences would also not line up:

123456  # position in ancestral sequence
ACGTAC  # ancestral sequence
CACGTAC # descendant sequence
 123456 # position in ancestral sequence

Before we can infer the evolutionary relationships between genome sequences, we need to first line them up properly so that each column of the "alignment" corresponds to the same position in the ancestral genome sequence. The reason for this is that, by aligning the genome sequences in this way, all nucleotides that are in the same column in the alignment are homologous (i.e., they evolved from the same exact nucleotide position in the ancestral genome sequence), and this knowledge of homology is the key insight behind inferring the evolutionary relationships between the genome sequences. In the examples above, we can add gap characters (-) at various positions of the sequences to get them to line up properly:

-ACGTAC # ancestral sequence
-GCGTAC # descendant sequence 1
--CGTAC # descendant sequence 2
CACGTAC # descendant sequence 3

Thus, as a first step, we need to perform Multiple Sequence Alignment (MSA): we need to line up our viral genome sequences. While this may seem like a simple problem at a glance, it's actually an incredibly computationally-intensive task to perform on large datasets. Fortunately for us, this dataset is small, so MSA can be performed quite quickly.

Step 2: Phylogenetic Inference

Once we have our Multiple Sequence Alignment (MSA), we can perform Phylogenetic Inference: using mathematical models of evolution, we can find the phylogeny (i.e., evolutionary tree) that best explains the evolutionary relationships between the sequences in our MSA. For reasons that are out of the scope of this workshop, the trees we produce when we perform phylogenetic inference are unrooted: they describe relative evolutionary relationships between the sequences (which appear at the tips, or leaves, of the tree), but they don't know the directionality of time and thus don't know where the root (i.e. the Most Recent Common Ancestor (MRCA)) exists.

Also, because we didn't provide the phylogenetic inference methods any temporal information (just information about mutations, represented within the MSA), the lengths of the branches in the evolutionary trees produced by phylogenetic inference methods are in unit of mutations (specifically, "average number of mutations per position of the sequence," or "number of mutations divided by sequence length").

Step 3: Rooting + Dating

As mentioned, the trees produced by phylogenetic inference methods are unrooted, and the branch lengths are in unit of mutations (not time). However, if we know the collection dates of the sequences in our dataset, we can use mathematical models of evolution to determine the most likely root as well as the most likely mutation rates across the branches in the tree. In other words, we can "root" the tree (i.e., find the most likely position of the Most Recent Common Ancestor (MRCA)) as well as "date" the tree (scale the branch lengths to be in unit of time).

Perform the Analysis!

As mentioned, you will be using ViralWasm-Epi to perform your viral phylogenetics analysis. This tool uses ViralMSA to perform Multiple Sequence Alignment, FastTree 2 to perform phylogenetic inference, and LSD2 to perform tree rooting and dating.

1. Download the HIV-1 or SARS-CoV-2 dataset
2. Go to the ViralWasm-Epi website: https://niema-lab.github.io/ViralWasm-Epi
3. For "Input Sequence File", select the _sequences.fas file from the dataset that you selected
4. Make sure that the "Skip Sequence Alignment" box is not checked (because we need to perform alignment)
5. For "Select Preloaded Reference Sequence", from the dropdown, select either HIV-1 or SARS-CoV-2 (depending on which of the datasets you selected)
6. Check the "Omit Reference Sequence from Output" box
7. Check the "Perform Phylogenetic Inference" box
   1. Make sure that the "Use Generalized Time Reversible (GTR) Model" and "Gamma Likelihoods" boxes are checked
8. Check the "Perform Tree Rooting and Dating" box
   1. For "Input Date File", select the _dates.txt file from the dataset that you selected
   2. If you're using the SARS-CoV-2 dataset, for "Outgroup File", select the sarscov2_outgroup.txt file and check "Remove Outgroups"
   3. Make sure that the "Infer Root" box is checked
   4. Keep all the other tree rooting/dating parameters as their default values
9. Click the blue "Run ViralWasm-Epi" button at the bottom
10. While the analysis is running, you will see the log messages get printed in the textbox to the right of the options
11. When the analysis finishes running, click the blue "Download Alignment", "Download Phylogenetic Tree", and "Download LSD2 Results" to download the output files

All of the output files are plain-text files (meaning you can technically open any of them in any text editor, such as Notepad or TextEdit), but they are not as meaningful on their own. Instead, we can visualize the results.

Output 1: Multiple Sequence Alignment

The Multiple Sequence Alignment can be visualized using AlignmentViewer.

1. Go to the AlignmentViewer website: https://alignmentviewer.org/
2. Click the "Choose File" button in the top-right corner
3. Select the file you downloaded when you clicked the blue "Download Alignment" button
4. Once the application loads your dataset, you can scroll left and right to view your alignment.

Output 2: Unrooted Mutation Phylogenetic Tree

The unrooted mutation phylogenetic tree can be visualized using phylotree.js:

1. Go to the phylotree.js website: https://phylotree.hyphy.org/
2. Click the "Newick" button in the top-left corner
3. Click the "Choose File" button
4. Select the file you downloaded when you clicked the blue "Download Phylogenetic Tree" button
5. Click the "Radial" button to the right of the displayed tree

You might be wondering why the tree was first visualized as if it was rooted. This is because the phylogenetic inference tools typically output the tree with some arbitrary root due to limitations of the output file format. It is up to the researcher to recognize that this root is meaningless (and to only interpret the tree as unrooted).

Output 3: Rooted Time Phylogenetic Tree

The rooted time phylogenetic tree can be visualized using Taxonium:

1. Extract the zip file that you downloaded when you clicked the blue "Download LSD2 Results" button
2. Go to the Taxonium website: https://taxonium.org/
3. Click the "Choose Files" button in the top-left corner
4. Select the phylogenetic.tree.result.date.nexus file you extracted from the zip file
5. Click the "Launch Taxonium" button