>> Welcome to Module 3.2,
"Getting Started with MicrobeTrace."
This presentation is part
of the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Ells Campbell,
and I'm a computational biologist at the CDC.
Now, this module is part of a collection of training materials
and resources meant to help you begin analyzing SARS-CoV-2
genome sequences.
Today I'll be talking about MicrobeTrace
and its use with genomic data.
I go through an outline here.
We'll begin by covering and introducing MicrobeTrace.
We'll begin by looking at transmission networks
and the different types of input files that you'll be able
to use, as well as diving
into some genomic data in MicrobeTrace.
And then, finally, a demo using simulated data
and then an introduction to resources for you
to continue your learning.
So what is MicrobeTrace?
Well, it's a powerful and secure data visualization tool
to allow you to map transmission networks, that exists solely
in your web browser and does not communicate information
over the web.
This tool handles various types of data inputs, encoding genomic
and epidemiologic data.
And it really stands as a user-friendly way
to explore COVID-19 data.
You can access the tool using a modern web browser
like Google Chrome using the URL at the bottom of the page.
Let's take a step back and cover transmission networks.
What are they and how are they useful?
Earlier in Module 1.2, we covered phylogenetic distances
between SARS-CoV-2 sequences,
and this is very useful information.
But it falls short in one particular way.
It is very difficult to integrate epidemiologic data
with phylogenetic trees.
And so, transmission networks are a way to render
that genetic data as a network
so that we can readily integrate it
with data commonly collected during contact
tracing investigations.
For example, Person A has had some kind of contact
with Person B. That integrates very clearly with virus
of Person A is this distance from virus of Person B.
So by integrating these two datasets,
it really allows a fine-grain resolution
of transmission dynamics in the face of an outbreak
so that we can design intervention efforts specific
to that outbreak, as well
as develop future prevention strategies
for outbreaks down the road.
Now, in COVID, oftentimes folks don't have a direct knowledge
of who they may have come in contact with.
And so MicrobeTrace enables you to encode data
that indicates a person's place of work or a place of residence,
wherever an exposure may have occurred, will allow you
to visualize and map that on top of your genomic data.
So the way that that is mapped is
with what we call an edge list or a link list.
An edge list is a contact tracing list.
It's really just Person A's ID, Person B's ID, and then the type
of contact that that may be.
One of those may not be a person.
It may be, you know, person-to-place.
And, if so, you may want to encode that information
as an additional column here.
And you can see we have contact types indicating both contact
tracing and person-to-place links.
So we'll cover an integrated example
of what this might look like.
First, we'll layer on the people and the places.
You can see the people represented as black dots here.
And then if we layer on the places, we can also see that,
you know, they're represented as squares.
This is the node-list information, you know,
a list of characteristics in Excel
to describe these individuals is perfectly sufficient.
Now when we layer on the edge list
that we described a moment ago,
we begin to see person-to-place links, and you can see that --
that not everyone is connected.
There may be disconnected clusters or groups.
Now we can layer on the genetic information
and show how those disconnected groups might share similar
genetic sequences here.
These are the pairwise genetic distances.
We can layer on more information.
For example, here we've sized nodes by how large they --
or how connected they are to other individuals.
We can take that a step farther and layer on other information,
describing how symptomatic or asymptomatic they are,
to get a better understanding of how this network comes together.
So let's look at the different ways
that we can import genomic data into MicrobeTrace.
Probably the most common
and most frequent is an aligned sequence file in a FASTA format.
On the left side, you'll see what that looks like.
It really is just a simple text string,
where you have the sequence ID followed by the sequence.
Now, these often come in an unaligned format,
and there is no difference between an aligned
or an unaligned FASTA other
than whether the sequences are aligned.
We'll get into the importance of making sure
that you have an aligned sequence in a moment.
We also accept distance matrices.
For the more advanced users out there
that may have their own analytic approach
to devising genetic distance,
you can collect your distance matrix, and we will import that
and render pairwise links.
And for those more comfortable with phylogenetic trees,
we accept the NEWICK format.
And what we'll do is take that tree and kind of crawl it
to understand pairwise distances on that tree
so that we can render it as a network.
So we'll get into each one of these in more detail.
The sequences often come
in a raw sequence format in a FASTA file.
And there needs to be some level of pre-processing
and quality control to make sure
that we're getting valuable comparisons.
And we'll walk through a few
of what those pre-processing steps might be.
Once you have the aligned sequence file, you can import
that right into MicrobeTrace and we'll visualize it for you.
So I'm going to give you an idea
of what a good-quality sequence alignment looks like.
You can see that, while all
of these sequences are actually identical, they are comparable.
Everything is in line.
But we commonly see, especially in the realm of SARS-CoV-2,
areas of the genome assembly that are of poor quality.
And that could be due to low sequencing coverage
in that particular region where we can't quite make
out whether it's an A, a T, or a G, or a C.
And so those are replaced with the letter N. Now,
if you have long stretches of Ns, like we have here
in this fifth row, you're going to need to remove those regions,
or, in some cases, remove sequences altogether
because they're really incomparable.
And so there needs to be some level of quality control
over the amount of Ns and how spread out they are
across the genome assembly.
Now, another common problem
that we see is just misaligned sequences.
You can see that there's kind of streaking or tearing
across this genome for rows two and four.
And they've kind of been shifted to the side.
And that is an example of a poor alignment quality.
We would want to make sure that those are properly aligned,
either manually or with a program.
And those are the types of things that you want to look
for as red flags in your analysis.
So to really get down to the details
of how we compare these genetic distances, in most cases,
at the sequence level, we're looking
at actual pairwise distances once it's aligned.
And when we see a difference in the DNA --
or RNA, we will count that difference.
And, for example, the pairwise distance here is four SNPs.
And so moving on to the next type of data format
that we typically accept are phylogenetic trees.
And you can download these from a variety of tools on the web,
most commonly might be Nextstrain,
which we covered in the last module.
And then there are the aligned sequences allow --
they allow you to build your own tree using any
bioinformatics tool.
So the important thing is we want to arrive
at this NEWICK file format for the tree
that can be loaded right into MicrobeTrace.
Now, there -- this is a very easy approach.
You can download trees from very reliable tools like Nextstrain.
Again, you can build your own.
And Nextstrain allows you
to download metadata along with your tree files.
But we do want to caution you that some web-based,
tree-building algorithms do not clean Ns from sequences,
do not necessarily check the alignment of the sequences
that you're handing it.
And they may lead misleading -- may lead to misleading results.
So we want to make sure that you understand the tree-building
process, and that it's done properly before you pass it
into MicrobeTrace.
And in much the same way,
we can render networks based on distance matrices.
A common tool is -- the kind of the guts
of Nextstrain -- is Augur.
Augur can export a pairwise, genetic-distance matrix.
You can, of course, generate your own from raw sequences
if you follow pre-processing steps
and then calculate those distances according
to whatever algorithm that is most valuable.
But ultimately, we need to arrive
at a distance-matrix format
that can then be imported into MicrobeTrace.
And this can be both as SNPs -- single nucleotide differences --
or it can be in terms of percent divergence,
or how distant two things are.
The key is that an increasing number leads
to increased distance.
I'm going to switch over to a demonstration
of MicrobeTrace using actual files.
And as you see at the top, we have a node list
that just describes the characteristics of individuals.
For example, we've got things like the facility ID --
if they're in a skilled nursing facility --
gender, and whether -- the patient's vitals status.
And then we also have a NEWICK tree.
Again, these are simulated data, but this will give you an idea
of what we're working with.
So once we drag those two files on, we can hit Launch -- --
and our network will come into view.
And initially, I kind of want to give you an introduction
to what the tool looks like.
So you'll notice that you're in a web browser.
But as soon as you jump in,
you will see nodes positioned on screen.
The nodes are positioned according
to a physics simulation.
The nodes push each other away.
The lines that connect them pull them closer together.
Now in the top left, you'll see a variety of interaction points.
First, you can freeze or pause the simulation
by hitting the pin -- the Pin All Nodes button.
And then you can actually dig in and start to play around
and look at particular individuals within the network.
If you get lost because you're zoomed too far out,
you can hit the concentric circles.
The same applies for zooming too far in.
If you find an image that you're interested in keeping,
you can hit the Download button and export it in a variety
of formats, including Scalable Vector Graphics.
But the real meat of the tool is this gear here,
where we can visualize data associated with the network.
So we can, say, add a label to the -- to these individuals.
But notice they're very much overlapping here.
You can't read it.
We can go over to this Network tab,
access the Physics settings, and start to spread the network out
and change some of these characteristics.
We can increase the length of these lines to get an idea
of how many people are here.
But this is a very deeply connected cluster
where almost everyone is connected to everyone.
And we want to fragment that.
And one of the ways that we can do
that is identify a genetic distance threshold relevant
to the particular pathogen of interest.
Here, we're going to pick 1.2%.
And it does begin to fragment the network.
You can see this group of four fall off to the side.
And this little node kind of pops out to the side as well.
MicrobeTrace offers this nearest-neighbor algorithm,
where it crawls the network and finds only the nearest neighbors
for each person and keeps only that link.
Now when it finds equidistant links,
it will keep all equidistant links.
And that's why we have some of these interconnected clusters.
But by applying the filter, we have now really narrowed this
down to kind of four independent-looking clusters.
So what we can do -- I'm going to take off the labels here.
We'll size nodes by how many neighbors they have.
So you can see the highly connected people
from the earlier part of the network there.
We'll color nodes by what facilities they came from,
and this is really critical.
So I can drag this up towards the top.
And we can start to see that, you know,
here we have one color -- if I pin all the nodes.
This group -- we can even put an ID label
of the facility as well.
So Group C really has multiple different clusters.
There's two distinct genetic clusters of COVID sequences
in Facility C. And so that means there's two independent
introductions that actually spread to other people.
Then we have a few introductions
in Community C that never spread.
These are kind of different variants of the virus
that just didn't quite transmit to a partner.
However, if we look at Communities A or Facilities A
and B, they both share, really, a very similar virus
that exists in both places.
That virus also transmitted to Facilities D, E, F,
and G. And so this is a much more, you know,
concerning pattern, whereas down here,
this seems like there might be some initial points
of introduction, but ultimately, those can be slowed.
And you can see here that we don't have a lot
of transmission.
So this is kind of a high-level use-case of MicrobeTrace
to get an understanding of what your networks look
like in the context of the genomic data.
And before we close, I want to describe some additional views
that MicrobeTrace can provide and that are very helpful
in analyzing epi and genomic data together.
For example, if you have data in the format of zip codes,
latitude and longitude, or state and county,
we can integrate your network with the map and kind of overlay
that on top of each other.
If you have timeline data, you can show Gantt charts,
as we've shown in the bottom left, to get an idea of duration
and events along a period of time.
We can also provide epi curves that aren't shown here,
as well as histograms that allow you to very rapidly get an idea
of an age distribution or something along those lines.
And finally, we have a list of links and resources for --
the URL to the tool is up here at the top.
Then we have a very long tutorial video,
where we go through each view and show you how
to work each particular setting.
And really, everything you want
to know is available on our GitHub page.
There's sample data on our GitHub page,
and there's also sample data that we demonstrated
with on the epi and genomic toolkit
that OAMD is providing for you.
And if you ever need support,
you can email microbetrace@cdc.gov,
and that email goes directly to me and the rest
of the MicrobeTrace team.
I want to go over some brief acknowledgments.
This is a very small team with really one developer
at any given time, and then a core team of people
for product development, testing,
analytics, communications.
But this is a very small team.
And then next, we're going to cover the next Module 3.3
of Real-Time Phylogenetics with UShER, and that's a part
of the COVID-19 Genomic Epidemiology Toolkit.
If you want to find further reading or subscribe,
you can follow the link that's on the screen.
Thank you.