Hello, and thank you for listening to the MicroBinfeed podcast. Here, we will be
discussing topics in microbial bioinformatics. We hope that we can give you some
insights, tips, and tricks along the way. There is so much information we all
know from working in the field, but nobody writes it down. There is no manual,
and it's assumed you'll pick it up. We hope to fill in a few of these gaps. My
co-hosts are Dr. Nabil Ali Khan and Dr. Andrew Page. I am Dr. Lee Katz. Both
Andrew and Nabil work in the Quadram Institute in Norwich, UK, where they work
on microbes in food and the impact on human health. I work at Centers for
Disease Control and Prevention, and am an adjunct member at the University of
Georgia in the U.S. Hello. Fancy meeting you here. Mine is probably not the
voice that you expected to hear when you tuned into the show, but that's because
today, the Canadians are taking over the Microbe and Food Podcast to tell you a
little bit about the SARS-CoV-2 sequencing and analysis in Canada. I'll be your
guest host. My name is Emma Griffiths, and I'm a research associate at Simon
Fraser University, and I'm helping to lead the data harmonization effort for our
national SARS-CoV-2 sequencing initiative called Cancogen. And I'm here with two
Canadian biomedics legends, Dr. William Chau and Dr. Finley McGuire. So Will,
would you like to quickly introduce yourself? Sure. My name is Will Chau. I'm an
associate professor at Simon Fraser University in the Faculty of Health
Sciences, and I co-lead the Cancogen metadata working group together with Emma.
So Fin, would you like to briefly introduce yourself? Sure. I'm the Donald Hill
Family Fellow in Computer Science at Dalhousie University. All this basically
means is I'm an independent postdoc that gets far too little supervision to do
largely things that interest me. So particularly in the context of the pandemic,
I've been doing a lot of work with Cancogen and the NML at the national level on
genomic epidemiology of the virus, as well as working with specific groups in
Ontario, such as McMaster and Sunnybrook, in particular looking at variants and
outbreaks there. So you're basically a man who gets around. Yep. So before we
dig into the technical details, I first wanted to do a little bit of a Canada
which I hope will put some of the following conversation into perspective. Some
things you need to know are that Canada is the second largest country in the
world by land mass. It's about 5,800 kilometres across. In fact, we're
representing the two coasts right now, as Will and I are in Vancouver on the
west coast, and Fin is in Halifax, Nova Scotia on the east coast. There's about
four hours and five time zones between us. Canada is made up of 10 provinces and
three territories, and we share a very large border with the United States, who
we are closely linked with through trade, travel, technology, the environment,
pathogen surveillance, and much more. We also have communities of Canadians who
originated or have family ties with countries all around the world, especially
Europe and Asia. And in fact, I think that applies to all three of us. And we
have communities of Indigenous Canadians who identify as First Nations, Inuit,
or Métis who, when it comes to pandemic surveillance, have important
considerations when it comes to knowledge and benefit sharing. And on that note,
I'd like to acknowledge that I'm speaking to you all from the unceded ancestral
traditional territories of the Squamish, Musqueam, and Tsleil-Waututh Nations.
And we'd like to thank our hosts for sharing these lands where we live, work,
and play. Listeners will be tuning in from countries all over the world, and
they may not be familiar with the COVID situation here in Canada. So, Finn, can
you tell us a little bit about what's happening here in terms of the pandemic?
So the pandemic is kind of, it's been quite variable across Canada. It's ranging
very much from the very densely populated provinces such as Quebec, Ontario, and
BC in particular, have had like the large burden of the cases, Alberta as well.
In total, there's been about 900,000 cases across Canada. Whereas then you have
provinces such as on the maritimes and the coasts, such as Nova Scotia where I'm
based, New Brunswick, PEI, Newfoundland, that have had relatively very few
cases. And really, especially with the reduction of flights, very much act as
kind of geographic islands. And that, so that's reflected, that kind of range of
case counts is very much reflected in a density of like genomic surveillance. So
for example, you know, Ontario where, you know, there's over 320,000 confirmed
cases, while they have done the majority of the genome sequencing, there's about
6,000 current public genomes from Ontario, that only reflects about 1.9% of all
their cases. Whereas provinces such as Nova Scotia, which has had 1,600
confirmed cases, but has actually managed to sequence 772 public genomes so far
and have sequenced more actually behind the scenes. So we actually have 46% kind
of coverage in genomic surveillance. There's a huge variety there. And then the
final kind of step of that is the degree of lockdown is hugely variable. So
currently in Halifax, I can actually go to the pub. I think there are limits or
something like we can have up to 50% capacity up to 150 people inside. You know,
we're not allowed to have festivals. Like that's the level of restriction, like
obviously that changes at times and if there's any surge, they kind of lock that
back down a bit more. But then that compares to say downtown Toronto right now,
which is in essentially full lockdown where you cannot gather with anyone
outside of your direct family. So the geography really means there's a huge
range of different manifestations of COVID-19 across Canada. Right. Right.
Canada has a decentralized health care system. And as you've already mentioned,
the public health orders are very variable across Canada for a lot of different
reasons. Now when I say a decentralized health care system, Canada has what's
called socialized medicine. So everyone gets a health number. You can go to the
doctor or the hospital for all kinds of different types of treatments and tests
and care for free. That also means that health care is considered to be under
the jurisdiction of the provinces and the territories. So they have authority to
say what tests are performed and what public health actions should be taken. So,
Will, what sorts of issues arise for SARS-CoV-2 sequencing in a decentralized
health care system? As you already mentioned, Canada health care system, it's a
federated system in that while the federal government provides the financial
support for the health care system across Canada, each jurisdiction is given the
mandate to carry out the health care for its population. And as such, it's also
deemed that the data that resides within the health care system and data
collected through the public health system in each of the provinces under the
jurisdiction of the provinces. So there are some, this is sort of the broad
interpretation of how our health care jurisdictions is set up. Although there
are some work being done to really highlight that indeed that's not the
intention of the system. The intention of the system is indeed one that will
have the federal government working closely with the provincial counterparts to
jointly take care of the Canadian population. But the interpretation of that is
often not, it's lost in the shuffle. So we do end up with a situation where each
province essentially has its own health care system and especially its own
health informatics system. And they're not always easy to share data from one
province to another or share data between the provinces or the territories and
its federal counterpart, in our case, the Public Health Agency of Canada for the
data associated with the COVID-19 pandemic response. Right, right. So in the
face of this decentralization, Canada has a lot of moving pieces when it comes
to SARS-CoV-2 sequencing, as you've already alluded to, Will. So can you tell me
a little bit about those moving pieces and how they intersect? So I'm going to
direct this question to both of you. I'm not sure who wants to start. Will, do
you want to start? Sure. So while we have a decentralized system, it's a system
that indeed provinces are communicating with each other, typically through a
setup of a network. So in the case of public health laboratories, there's a
network called the Canadian Public Health Laboratory Network, or CPHL. And this
network is set up to consist of all the provincial and territory public health
labs and the federal lab, namely the National Microbiology Lab in Winnipeg. So
through this network, there are regular communications about various topics
related to the pandemic response, and also informatic systems such as SINFI,
which is an epidemiological oriented information gathering system. So
essentially an epi-intel system has also been created. And there are other data
collection instruments that are being used to gather information across network.
Although often in the absence of such devices, paper-based form are usually then
captured in Excel spreadsheets or other sort of ad hoc information gathering
devices and shared that way. So by no means are we a coherent and cohesive
network when it comes to information sharing, but nevertheless, it is a highly
collaborative and highly interactive network.  And so where a lot of that
collaboration has kind of really started to, has manifested itself is in this
Umbrella Cancogen organization. So this is a Genome Canada, which is kind of one
of the funding agencies in Canada, particularly focused on genomics, as you'd
think from his name, that has actually a similar, similar to our kind of
decentralized federal provincial structure, Genome Canada is also divided into
different sub bodies that cover different areas across Canada. So largely
through a lot of us already working together with the NML and provincial
partners on infectious disease, genomic epidemiology, through work with IRIDA,
integrated rapid infectious disease analysis platform, which I always get wrong.
A lot of us were kind of brought into this Genome Canada funded Canadian COVID
genomics network. And really that was kind of set up to kind of coordinate how
we're doing data collection, coordinate how sequencing's happening across the
country. And the sequencing is happening at whole mix of these different
partners. It's happening centrally at the NML. Some provinces are sending
everything to, to the NML. So Nova Scotia, for example, are not doing that
sequencing locally. What they're doing is they're kind of packaging everything
up, sending the NML. And then the NML is kind of doing the sequencing and
analysis for them and then feeding that information back to them. Whereas
provinces such as BC and Ontario and Quebec, which have a lot of that
infrastructure in their own big, in the larger scale public health labs and
agencies, sort of doing that kind of sequencing themselves, and then maybe
feeding data to the NML and Public Health Agency of Canada for kind of broader
federal coordination. So yeah, so Cancogene is really the kind of the
coordinating arm and yeah, as well as the provinces, we also have individual
kind of academic partners like the Ontario Institute of Cancer Research is doing
a lot of sequencing, much as McMaster University, really led by Andrew MacArthur
there. Some of you might know from the CARD database and AMR work, it's really
transitioned to do a lot of virology. So yeah, so we have lots of meetings and
there's all these different working groups, such as data analysis, the metadata,
which Will and Emma are really both key members of. Yeah. So that's kind of the,
and Cancogene also does include non-viral seq. It's really, it's also meant to
be doing so-called host seq. So that's doing human genome sequencing on infected
people to look at the kind of epidemiology from that side of COVID-19. So
linking like the pathogen side to the host side to see how both of the genomic
mutations and genomic effects can impact outcomes. And then also like again, in
our, you know, in the slightly fragmented federated system, there is also some
provincial, like specific provincial efforts, such as the ONCOV, so the Ontario
Rapid COVID Coalition, which a lot of the COVID stuff, but it's a lot of the
same people that are also contributing to Cancogene. So they're not, they're not
totally separate. They're very nested in each other. And again, there are lots
of services being run through that. There's private Nextstrain instances host
the NML for each province. So they can see their own data in that evolutionary
context. There's public Nextstrain instances for Ontario and Canada as a whole,
which I kind of host and are listed on the Nextstrain website. So there's a
whole bunch of different kinds of services that all kind of fit underneath that.
So there really are a lot of moving pieces, the Cancogene and the sequencing
effort in Canada. Maybe could add quickly that before COVID-19, there's already
some capacity for doing genomic sequencing and also doing genomic analysis
within the public health system, chiefly through the coordinated effort of the
Canadian PulseNet, which focuses on foodborne diseases. But with the pandemic,
the idea is to have Cancogene act as a coordinating body to bring together both
genomic experts in the academic community with the public health experts and
also with healthcare experts in the hospital and so on, and coordinate the
effort to use genomics to address both health, health, but also pathogen
evolution related challenges in question. Drilling down on the viral data a
little bit more, how is it being stored and analyzed for national surveillance?
I guess, Will, do you want to take that? Sure. So as Finn already mentioned, in
Canada, the large provinces, namely Alberta, BC, Quebec, and Ontario are doing
its own sequencing in-house or in partnership within the province. As a result,
sequences are generated at various sites across Canada. And there is a national
database that's hosted at NML that's meant to bring together all the data to a
central site for national surveillance. And as already previous mentioned, NML
is also doing sequencing on behalf of the smaller provinces that currently does
not have genomic sequencing capability in-house. And therefore, the national
database act as an aggregation site for the data. However, there are some
challenges with storing the data nationally, namely data sharing agreements need
to be established across provinces with the federal site. So even though the
technical setup for the database was in place a month ago, it's still an ongoing
process to ensure that the national database indeed has all the sequence data
from the provinces. But in the absence of that, right now, a subset of the data
is actually being sent directly to get set and published internationally. But I
could add that the effort had been made through the CanCoGEN coordination that
fairly soon there will be indeed a coordinated effort to make the Canadian data
available through a national data portal that's hosting Canada and will consist
data that will be publicly available. Largely modeled on the kind of great,
great example of that we have from COG UK, as well as like the opportunities of
integrating that with other microreact framework. So we can do a lot of those
analyses very much quicker rather than kind of a bit more of an ad hoc way where
we have to pull down that mix of data from GSED from different submitters, et
cetera, and try and coordinate all that. So as we're talking about the portal,
we're talking about all these different players, data sharing frameworks. I can
just imagine that, you know, all these different pipelines, there's all this
different instrumentation that's being used, presumably there must be different
quality control metrics across all of these different submitters. So basically,
how are we dealing with all of that, Finn? The fundamental thing is we kind of
let anarchy reign to some degree, in terms of the actual analysis methods that
are happening. But then we kind of, we develop through the quality control
working group in CanCoGEN, led by Jared Simpson at OICR, kind of really
developed a kind of quite solid set of baseline QC metrics that all of the
genomes have to kind of ascribe to, focused on both issues in data prep and
controls, actually analysis, like RNA extraction, et cetera, all the way through
to like the bioinformatic analysis and de-hosting. We also did develop some like
standard tools for trying to improve the use of de-hosting. So that included
nanostripper for nanopore data at the NML. And then also within the context of
the signal workflow, worked very carefully, again, with Jared, trying to develop
a sort of competitive mapping solution. So we managed to kind of, between the
host human genome and the viral genome references, to try and deal with a few of
those reads that were kind of, that were mapping to both. And by having a
competitive mapping, instead of just mapping to human and throwing away
everything that maps to that, or mapping just the virus and throwing away
everything that doesn't map to that, as some approaches have been done, that
really kind of let us keep some of those like in the middle, in the middle reads
that had a better mapping to virus than human, but did map to both. So a lot of
work on that de-hosting. And then kind of, then finally the kind of, the main
kind of over time, especially through discussions of the data analytics workflow
and some of the CPHLN calls, the data analysis kind of core kind of standardized
and crystallized, not just as it did across the world, to be honest. We kind of
started with very disparate workflows. I mean, everyone kind of crystallized on
something quite similar to the, you know, the Arctic kind of Connor Lab kind of
next floor workflow with that core of the IVAR BWA kind of approach for doing
variant calling consensus generation. Like then a lot of the others, it was just
kind of framework and infrastructure around that common core, which meant at
least the data was relatively comparable and the QC metrics could be pretty
robustly done over the data without having to like deal with like very different
types of data. I mean, you've already kind of mentioned some details about the
tools, the platforms and pipelines that people are using. Do you, are there any
other comments that you want to make, any more details that you want to provide
about that? Sure. So, I mean, I mean, everyone, everyone is doing the Amplicon
sequencing as we see across the world, largely the article protocol, but we are
seeing, we are seeing variation in the actual primer schemes being used, free
resend scheme. Some are using the Illumina COVID-Seq and that's largely down to
individual lab preferences, individual performance preferences, the throughput
they're wanting, what they have available. So there's a, there's a big
assortment of different sequence prep kits and instrumentation being used. It's
mostly Illumina and Nanopore again, as you'd expect, but there has been supply
chain disruptions as many people have faced across the world from pipette tips
to specific reagents. So again, that's affected the way kind of that's been done
as well as just expertise, especially in some of the smaller partners. We're
really trying to ramp up sequencing in individual hospitals now. So sometimes
the level of tooling and equipment is not the same as a, you know, BCCDC
sequencing kind of core, like there's a big variety there. As I say, there's
kind of, there's different pipelines. There, there's a mix, you know, Quebec
have their kind of own one. BC uses, I believe a variant of the ConorLab
NextFlow. Ontario uses a mix with ConorLab NextFlow workflow. There's Signal,
which I'll talk about in a second. Again, the same core originally was developed
to try and handle when we weren't quite sure how sequencing was being done. So
it was originally developed as a workflow.  handle all these different types of
sequencing data, you know, the metagenomics approaches, the bait capture, the
amplicon tiling, et cetera. So originally I developed Signal for a project led
by Julie Sincere in Andrew MacArthur's lab, really trying to look at how to get,
how best to do sequencing right back, right back early in the pandemic. And the
answer was basically the Arctic Protocol, which was convenient. But that meant
we then had this workflow that we started to build kind of sequencing
infrastructure around that implemented in Snakemake. So we kind of coordinated
that same core. I feel guilty for contributing to the proliferation of
workflows. I know, I know it's bad partitions. We should never really do that,
but we built it for a different purpose originally. And then it was repurposed
to kind of standardize them by common core. And it does like, it's in Snakemake.
So that is an advantage. Some people were more comfortable with Snakemake versus
Nextflow. There's a lot of debate which is better, but the Nextflow, you know,
the Conor Lab Nextflow really has a lot more eyes on it, a lot more kind of high
level robustness, I would say. So, you know, if you're starting blank, I would
go for that. But Signal also does kind of a bit more, it uses that competitive
mapping for de-hosting. So I think it's a slightly better de-hosting method. And
it also has kind of more generation of interactive reports, which is not really
needed in the cog system because you, because there's such great infrastructure
for doing all that at a higher level. But for individual labs, there's been a
lot of kind of interest and popularity in those kind of interactive reports that
summarize that particular run of all those genomes. So yeah, so there's a
variety of different kind of ways and approaches being done across Canada. At
this point, I kind of want to switch gears a little bit and start to talk about
variants. So in Canada, we're seeing a lot of B117 and we've had cases of B1351
and P1 pretty much since they were first identified. So what can you tell us
about what we're seeing in terms of the prevalence of what we call the big
three? And what can you tell us about any new variants of interest? So Finn, I
know you've been doing a lot of work on this for different provinces. Do you
want to expand on that a little bit? Sure. I might actually just jump back
briefly, and I apologize if this means editing. Because one of the things, and
particularly with Jared, in terms of the Canadian workflows is we've been having
this issue, this kind of dive slightly into the more technical chat, having this
particular issue with indels and variant calling around indels by IVAR. It's
been submitted as issues, but essentially it's kind of standard. It's the
standard pile up method related variant calling error. So in short, it's just
when you have an indel and you have reads that your scoring scheme for alignment
doesn't like introducing a big gap in the read. So you tend to get really bad
alignment around that indel for a few of those reads that map it, which leads to
these spurious variant calls. So Jared tried to kind of work around ways and
much better at this kind of stuff, and obviously has a lot more experience in
the weeds of some of the variant calling methods. But it's actually made a nice
fork of the ConorLab Nextflow workflow that implements a Freebase alternative,
but then processes the results of that. So essentially, instead of avoiding
indel alignment, realignment issue, does basically gets accurate variant calling
around the indel. But adheres to the same masking and thresholds as the IVAR
tool. So we're currently running that in parallel. So again, through the OICR
fork of the Nextflow workflow. I've also implemented it in Signal in the dev
branch currently. We're just kind of tuning it and checking that out. We're
probably not going to leave IVAR. We're probably still going to do the IVAR
based calling. But really, I think this is useful for kind of checking some of
the more spurious kind of mutations that IVAR can sometimes predict around
indels. It's nice to have this as kind of a side check. And it's a lot quicker
than our previous side check in Signal, which was Briseek, which is a great
tool, but it's not a fast tool. So yeah, that's one of the main kind of workflow
things and things in Canada that we're changing, where we're changing things
rather than just repurposing things the rest of the world's using. So just to
jump back to variants, do you want to tell us a little bit about that situation?
Sure. Overall in Canada, we're seeing the same kind of variant of concern kind
of increase over time as being seen in many other countries in the world. So
currently, Ontario, I think, is one of the higher percentage, although it's not
certain based on exactly how deep monitoring is across provinces and doing that
comparison can be quite difficult. There's about, I think there's two, yeah,
there's about 6,000 cases in Ontario currently of one of the variants, three
variants of concern and almost exclusively B.1.1.7. There's a small number of
B.1.351 and P.1 variants across the provinces in Canada, but most of the
variants of concern genomes that we're seeing are B.1.1.7. So that's about 42%
of all the Ontario cases, or genomes that we're sequencing are B.1.1.7. None of
them seem to have the E4K mutation yet in that particular combination. But yeah,
so we're seeing it basically increasing at the same rate that we see everywhere
else. We see the same issue of faster transmission of B.1.1.7, possibly more
severe disease, kind of, again, following a lot of those patterns that we've
seen. One area that's been quite like a small number of cases overall, but quite
a significant outbreak was in Newfoundland, where there was, it had almost, I
think, I'm not sure, have they had any cases? It had almost no cases in the
pandemic, but up until very recently, and then there was quite a large outbreak
that is largely associated with the B.1.1.7 variant. So it spread very quickly,
which, especially in the maritime communities that had possibly been a little
bit more, or Atlantic communities, I guess, because we're talking about
Newfoundland as well, that had possibly been a little bit more casual or
laissez-faire at times, now that, because we didn't have a lot of cases. It was
a useful reminder that especially these higher transmission variants can spread
very quickly in these smaller communities, these quote-unquote islands. You
know, everybody and their dog have their own variants these days. Is there such
a thing as a Canadian variant? I mean, there's definitely, there are definitely
variants in Canada that we probably aren't seeing elsewhere, or certain
mutations occurring in Canada. You know, there's a lot of cases, and we don't,
you know, in some provinces where, you know, in the one to two percent of cases
are getting a genome sequenced. Like, we've been using genomic data to develop
kind of high throughput PCR screens for, you know, F501, N501Y. There's a new
one, just Hamilton Health, we've got E484K kind of screen mutation. So, and, you
know, so we're developing some kind of faster ways to kind of screen, trying to
look for those particular mutations we know are potentially concerning and seem
to be arising independently in multiple lineages. But there's definitely,
there's, I mean, you know, it's an evolving, it's an evolving entity. There's
going to be variants in Canada that we don't see elsewhere. Right. You know,
we're working very carefully to try and, you know, triage them and kind of work,
and this is where a lot, a lot of the work that Em and Will have been doing
about like variant nomenclature, again, a pattern being repeated across the
world of how do we classify and talk about variants? But yeah, we're screening
them, we're looking for them, you know, we identified, you know, we identify,
you know, the B1525 that, you know, this other lineage that had an E484K
mutation that, you know, we hadn't really seen before, but seemed to be, you
know, increasing in numbers. So, you know, and then with that led to
coordination chat, you know, talking with other countries that possibly detect
it, looking at travel history across the provinces and really bringing in
conference calls, ad hoc conference calls with all of those public health,
different public health agencies, especially the ones doing their own sequencing
to really talk about like, okay, what data have you actually got versus what
data have you managed to like get through the hoops to submit to a database or
release yet? So kind of trying to do that as fast as possible and kind of kind
of close that loop, even in some of the issues of data sharing to better monitor
new variants. In Canada, we're aiming to sequence about five to 10% of our
samples. So some argue that might be too low to detect the rare variants, but I
think the key here to understand is what we're interested in is variants are
likely to increase in terms of increase in the population. So even at that level
of sequencing, we should be able to spot new variants that are increasing in the
population. And also importantly, as Finn mentioned, we are screening for known
variants using more high throughput methods than sequencing to allow us to
quickly catch some of the variant concerns in the population. But I also want to
highlight that this is again a call for a need for a centralized Canadian
database in order for national profiling of the different mutations that are
seen in the population. So the power of aggregation then would allow us to
detect the low and rare mutations in the population before it becomes a problem.
And I just want to circle back on one of the things that Finn mentioned that has
been discussed on this on the Microbe and Food podcast before, the issue of
variant nomenclature. So how are we handling variant nomenclature in Canada? I
know, Will, you've been doing some work on that. Do you want to make sure? So
indeed, one of the activities that was undertaken as part of the CPHON is to
standardize the names given to the different variants that are found in Canada,
and especially ones that are a variant of concerns. So we, given that many of
the provinces have implemented PENGLIN as the lineage calling tool, and the
decision was then to adopt the PENGLIN lineage naming scheme for reporting
purpose. So currently, we have the three variants of concerns named using the
PENGLIN lineage names, and then the others, we're referring to them simply as
variants or variants of interest.  with no formal system to prioritizing them,
but still to give them consistent names. So if they come up again and again, we
will have the ability to spot the increase and to compare the variants across
Canada. There are also works that are going on as part of the CanCoGen network
to link variants to their clay defining mutations and also linking these
mutations then to the functional changes that might occur by the variants. So
indeed in Canada, we very much are interested in the evolution of the virus and
are trying different methods to capture these changes and to name them
consistently across Canada, adhering to the international standards and
conventions as much as possible. I've been really trying to advocate for a sort
of two separate classification systems in which we kind of triage individual
mutations. And we use that to feed into our variant of concern, variant under
investigation type, type like triage of variants specifically, so we can then
prioritize those follow-up experiments, those further epidemiology. But just by
having those kind of two systems, I think it's a nice way of kind of dealing
with that mutation versus lineage kind of gap. Yeah, indeed the emphasis on
variants sometimes undercut the importance of it's actually the mutations and
the constellation of mutations that are defining these variants. So indeed the
underlying evidence, the underlying mutations need to be highlighted and tracked
as well. And we don't really have a process for going from a variant of interest
to that higher priority variant of concern level yet, but we have a lot of
discussions ongoing and we're certainly looking to the international community
for guidance on that as well. We wanna make sure that we're in line with that.
Kind of international consensus and building that global consensus, we also
have, as I say, the different public health systems and the different provinces
and territories are also approaching this like, not necessarily all approaching
this exactly the same way. So I know some of this can be resource-based, in a
larger system like Ontario, they might be triaging a larger number of variants
and mutations than another province can like afford to actually dig down that
far. Hi guys, it's Nabeel up here in the editing booth and I think that's all
the time we have for today. The Canadians have taken over the podcast this
episode to talk about their public health response and genome epidemiology of
COVID-19. I want to thank Emma, Will and Finn for running the show today and you
will hear more about COVID-19 in Canada next time on the MicroBinfy podcast.
Thank you all so much for listening to us at home. If you like this podcast,
please subscribe and like us on iTunes, Spotify, SoundCloud or the platform of
your choice. And if you don't like this podcast, please don't do anything. This
podcast was recorded by the Microbial Bioinformatics Group and edited by Nick
Waters. The opinions expressed here are our own and do not necessarily reflect
the views of CDC or the Quadrant Institute.