>> Hello, and welcome to Module 3.3,
"Real-Time Phylogenetics with UShER."
This presentation is a part
of the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Dr. Russ Corbett-Detig,
and I'm a professor of biomolecular engineering
at the University of California, Santa Cruz.
This module is a part of a collection of training materials
and resources meant to help you begin analyzing SARS-CoV-2
genome sequence data.
Be sure to check out the toolkit's other modules as well.
In Module 1.3, we covered the basics of building
and interpreting phylogenetic trees,
and we briefly introduced Nextstrain as a powerful tool
for interactive visualization of phylogenetic trees.
As viral genome sequences are rapidly accumulating,
such phylogenetic analyses are increasingly fundamental
for interpreting viral transmission dynamics.
However, the volume of data has made phylogenetics
increasingly challenging.
In this module, I'll describe a tool that my team has developed
to facilitate real-time phylogenetics
of newly sequenced genomes.
It's called UShER, for Ultrafast Sample placement
on Existing tRees.
Briefly, UShER is designed to do three things.
First, UShER accurately places samples onto a global phylogeny.
Second, UShER constructs new subtrees
when added samples are closely related.
And third, UShER enables easy visualization using a
web interface.
Importantly, UShER is fast enough
to place sequences in real time.
Samples are typically placed in just tenths of a second.
And as a result, our tool can empower rapid
genomic epidemiology.
Before I get into how UShER works and why it's so fast,
let me walk you through why a pandemic is challenging
for a typical de novo phylogenics workflow.
For a simple illustration of how phylogenetic trees are inferred
from molecular sequence data, and a description
of their parts, be sure to visit Module 1.3 of this toolkit.
The way this typically works is
that a user accumulates their dataset,
then infers the phylogeny.
But by the time your tree is ready,
in a pandemic it's already out of date.
And a whole pile of data has to be added to it
to interpret newly sequenced genomes.
So you can add these new sequences to the dataset
and start over to infer a new phylogenetic tree from scratch.
But the same thing happens again,
and your phylogeny is already out of date
by the time it's finished.
Moreover, with each addition to the dataset,
de novo tree inference becomes harder and it takes longer.
UShER solves these problems by using a new approach
for phylogenetics, where we add newly sequenced samples
to an existing phylogenetic tree,
rather than inferring a complete tree de novo.
To start, UShER takes as input a phylogenetic tree
and a set of samples.
In this example, samples are labeled A through D,
with each sample represented as a list of its mutations relative
to the ancestral genome sequence, which we can get
from a multiple-sequence alignment,
as is used by most phylogenetics applications.
So for example, at the second position in this alignment,
sample D carries the ancestral sequence,
and all others have a substitution to U at that site,
represented by the phrase 2U in the in the list
of mutations at the right.
UShER uses this input to infer the positions of each mutation
on the input phylogenetic tree.
For example, this mutation circled in red is the one
that occurred at position 2.
The substitution to U is shared by samples A, B,
and C. So we can surmise this mutation occurred on the branch
of the tree where these three samples are descendants.
We call this finalized object a mutation annotated tree,
where each branch is labeled with a number on the tree,
and the mutations that UShER inferred to occur
on that branch are listed to the right.
Then, when a new sample is sequenced,
rather than infer the full tree, UShER is able
to add this new sample to our existing tree.
To do that, we provide UShER with the list
of mutations present in the sample.
UShER compares that sample to every branch in the tree
and identifies the branch
that requires the fewest additional mutations.
In phylogenetics terms, we call this the maximum
parsimony placement.
Here, you can see that this sample shares one mutation
with sample D, so it should be placed on that branch.
Importantly, UShER is then able
to write the new phylogenetic tree,
including the user-supplied sample.
And of course, all large phylogenetic trees almost
certainly contain errors.
But we have found that our approach has comparable accuracy
to more conventional de novo phylogenetics methods.
You can read more about this in our manuscript,
for which I'll provide a link at the end of this presentation.
We have found, in general, that sample placements
under the tree are exactly correct in 97% of cases.
And when they are wrong, the placements tend
to be very nearby on the tree.
So we expect that most users will want
to closely examine the placements
of newly added samples.
UShER therefore outputs a subtree
of the 50 samples already in the tree
that are the most closely related to the user's samples.
Here I'm showing you what those trees can look
like using the Auspice interface
where I highlighted the user-added samples in red
to distinguish them from other samples in black.
In this case, we see that the three user-uploaded samples are
each other's closest relatives,
possibly suggesting transmission among the sampled individuals.
More information about using Nextstrain
and exploring the Auspice interface can be found
in Module 3.1 of this toolkit.
In addition to accurately placing high-quality samples,
UShER can be used to quickly identify
and remove low-quality sequences.
Here I'm showing you a results table
that our web interface produced when UShER attempted
to place user samples.
The first metric I want to draw your attention
to is the parsimony score.
This is the number of mutations
on the branch leading to a user sample.
When the value is high,
our output will highlight the box in red, as shown.
This generally suggests that the sequence has many errors,
and sample placements might not be reliable.
In the toy example here, you can see that makes it
so that sample E is added to the tree on a very long branch
with many unique mutations.
The second quality control statistic is the number
of equally parsimonious placements for a sample.
That's the number of placements on the tree
that require the minimum number of added mutations.
You can think of this as a measure
of the uncertainty inherent to a sample.
Usually this happens when a sample has been contaminated
with a strain that differs genetically.
And in the example shown here,
because each site has missing data,
placements on each branch are equally good fits
for the sample.
So to make UShER easy to use, we provide a web application
that takes the sequence upload from a user, places samples
onto the tree, and then displays a summary of the results,
as well as subtrees for each sample or set
of samples provided by the user.
To upload your data, simply select the "Choose File" button
that I've highlighted here,
and choose your FASTA file containing
up to 100 SARS-CoV-2 consensus genome sequences.
And then select "upload."
Note that you are uploading samples to our interface,
so the sequence data do leave the computer you are working on.
We do not share your data with anyone,
and we delete the results unless you choose to save them.
If you want to have your samples permanently incorporated
into the tree, we encourage you to upload your sequence data
to the public sequence repositories such as NCBI
and GISAID, from which we draw the data that we use
to maintain our comprehensive phylogenetic tree.
To show you how this works in practice, I'm going to switch
over to another tab on my web browser
and show you a demo of UShER in action.
I've opened a new tab on my browser,
and to get to the UShER web portal,
I suggest you just type "ucsc usher."
And the very first link that pops up will be our web portal.
You can also get here using the link I'll provide at the end
of this presentation and the link
on the webpage associated with this module.
Now to upload your data, select the "Choose File" button,
and navigate to the place on your computer that contains all
of your consensus sequences
in FASTA format, and select that file.
You'll notice that UShER has a drop-down menu
with different public trees that you can add your sequences to.
We do this so that we'll maintain older trees
so that you can reproduce results from previous analysis.
In this case, I recommend you just use the top,
most up-to-date tree, and select the "upload" button here.
And UShER will take just a few seconds to add your samples
to our global tree and compute summary statistics
about those samples for you.
All right.
It's already done.
So we've uploaded five samples, and each of them is shown
in this summary table here
with various statistics computed for that sample.
You'll see that each of these boxes is green
and one is yellow, suggesting these are reasonably
high-quality samples.
So for example, this column here is showing you the number
of bases aligned to the reference sequence.
And if you hover over the little question mark,
you'll see a description pop
up of what this statistic is telling you
about this sample's alignment and placement on the tree.
If we go over here, you'll see the two statistics identified
earlier as particularly important.
They are the number of maximally parsimonious placements
on the tree and the parsimony score associated with the sample
with the number of mutations on the branch added
by adding that sample.
You see both of these are relatively low numbers.
There's exactly one equally parsimonious placement for each
of these samples, and they add relatively few mutations
to the tree.
So these are pretty high-quality samples.
If we scroll down, UShER will produce results for you
for each set of related samples here.
And so it has a number of useful statistics, for example,
the path of the mutations from the root of the tree
to your sample, and also the most closely related sample
in the public tree, and the pangolin lineage
that has been assigned to the sample in the public tree.
And if you want to view this result in Auspice,
select this "view subtree 1 in Nextstrain" button here.
And you'll see that our tree pops
up in the Nextstrain Auspice interface.
By default, we color every sample
in the public tree using the pangolin lineages, so it's kind
of a rainbow of colors.
If you want to make your samples pop out against this background,
I suggest you select the "Color By"
and "Sample type" option here.
And what will happen is that the user-uploaded samples will be
highlighted in red, and every sample already
in the public tree will be in black.
So in this case, you can see
that our user-added samples are three
that are very closely related to one another.
In fact, two of them are genetically identical,
here and here.
And the other sample is very closely related here,
suggesting these may constitute a recent set of transmissions
within a local community.
You can also see that the most closely related sample
to these two is here.
It's a sample from Minnesota, and information about it pops
up by hovering over that sample as well.
If you want to see where your other samples place
in the global phylogeny, if you scroll down,
you'll see that the other two samples that we uploaded --
remember there were five total --
are placed in another subtree: subtree 2.
And we'll compute the exact same statistics
for that set of samples as well.
And if you select the "view subtree 2
in Nextstrain" option here, a new tab pops up again,
showing our subtree that we produced
in the Nextstrain Auspice interface.
And if we do the same sort of thing --
where we go to the "Color By" menu and scroll down to
"Sample type" -- you'll see our samples highlighted here in red
and every sample already in the public tree in black.
And in this case, you see that the two samples
that were uploaded by the user are pretty closely related
to each other.
And they're also very closely related to another sample
from Florida that you can see information
about by hovering over here.
So to quickly recap and summarize, UShER is a tool
for rapidly placing new samples
onto an existing phylogenetic tree.
The main use we foresee
for public health is quickly determining relationships among
samples and identifying close relatives
in the existing phylogeny.
UShER can also be a powerful quality-control tool.
The URL to the UShER web portal is listed again here
and on the toolkit webpage.
There you will also find links to example data
for hands-on exploration of UShER.
So UShER has really been a team effort,
and a lot of people deserve credit for this work.
In particular, I want to thank Yatish Turakhia, who wrote
and maintains the core UShER program, and Angie Hinrichs,
who developed the web interface.
I also want to thank the rest of our team
and various funding sources that have supported our work.
This concludes Module 3.3.
Part three of the toolkit focuses on useful tools
and skills for applying SARS-CoV-2 genomic data
in epidemiological investigations.
Please visit the COVID-19 Genomic Epidemiology Toolkit
page, where you can find further reading on the topic.
On the toolkit page, you can also subscribe
to our mailing list and receive announcements as new modules
and materials are released.
Thank you.