>> Welcome to Module 3.5,
"Public Genome Repositories for SARS-CoV-2."
This presentation is a part
of the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Dr. Michael Weigand
and I am a bioinformatician with the CDC.
This module describing public sequence data repositories is
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,
which include a combination of case studies
and training materials to help you get started supplementing
epidemiology with genome sequence data.
In the welcome module of this toolkit,
AMD Director Dr. Greg Armstrong highlighted the rationale
for sequencing SARS-CoV-2
at both the local and national scales.
Much of the toolkit's materials focus on the local scale
and applications of sequencing for genomic epidemiology.
Importantly, collecting
and openly sharing genomic sequence data can both
supplement local investigation and act as the link
to connect local activities to support broader,
national public health efforts, like strain surveillance
and monitoring diagnostic performance.
We have witnessed unprecedented sharing
of viral genome sequence data across the globe from the outset
of the COVID-19 pandemic.
This open data sharing empowers real-time snapshots
of the evolving virus population with tools like Nextstrain
and Pangolin, and has enabled rapid,
successful vaccine development.
Therefore, depositing sequence data
into public repositories should be an integral step
in the SARS-CoV-2 sequencing workflow at any laboratory.
And the importance of attaching consistent clinical
and epidemiological metadata cannot be overstated.
Although this module does not cover the details
of data submission, some links
to helpful resources are included on the toolkit webpage.
This module looks at two public repositories commonly used
for collecting and sharing SARS-CoV-2 genome sequence data
and associated metadata.
The first is GISAID, which stands for the Global Initiative
on Sharing All Influenza Data.
As the name suggests, it was born from the desire
to facilitate rapid data sharing for flu viruses collected
in what's called the EpiFlu database.
That existing infrastructure was quickly adapted
to also accommodate SARS-CoV-2 sequence data,
organized in a similar database called EpiCov.
The other is NCBI, which is the National Center
for Biotechnology Information, a division of the National Library
of Medicine within the US government's National Institutes
of Health.
Many are likely familiar with at least some
of the other biomedical resources provided by NCBI,
like Genbank and PubMed,
or sequence analysis tools like BLAST.
As we'll see, the two repositories are organized
differently, and their SARS-CoV-2 sequence data are
often used for different purposes.
Unfortunately, there's no direct standardized link
between records shared in the two,
and thus many laboratories submit sequences to both
so users of these data may need
to perform de-duplication before certain analyses.
Both repositories maintain indexed collections
of SARS-CoV-2 genome sequence data and associated metadata.
This module highlights some key features of each and tips
for searching these databases for sequences
to supplement local phylogenetic analyses or investigations,
which can be particularly useful in contexts
where sequencing is being performed
by multiple institutions, like public health
and university laboratories.
This plot illustrates the number
of assembled SARS-CoV-2 genome sequence entries submitted
to GISAID and NCBI from laboratories
in the United States, spanning the months
from June 2020 to March 2021.
Both repositories continue to grow rapidly,
with a clear acceleration at the beginning of 2021 as interest
in new variants and national surveillance began in earnest.
GISAID hosts a larger collection of entries and continues
to receive the most submissions, likely reflecting the ease
and speed with which the repository was adapted
from its original design for collecting influenza data.
Whether or not you're familiar with GISAID,
you've likely already encountered some benefits
of the EpiCoV database because certain applications popular
for monitoring SARS-CoV-2 evolution
and genomic epidemiology depend on these data.
For example, many public Nextstrain trees,
including those maintained by the CDC SPHERES consortium,
are calculated with sequences pulled directly from GISAID.
And similarly, PANGO lineage definitions,
which are often used to describe notable SARS-CoV-2 variants,
are also calculated using a collection of GISAID sequences.
It's likely because these tools pull directly from GISAID
that many SARS-CoV-2 sequence data continue
to be preferentially submitted to that repository.
This screenshot of the GISAID website shows an overview
of what's included within the EpiCoV database.
This module will look briefly at the search function,
accessible here at the top,
but know that GISAID regularly performs several analyses
and provides some summary reports of the database,
just a few of which are listed here.
GISAID's EpiCoV database is huge.
On the day this module was recorded,
there were over 850,000 entries from around the globe.
Thankfully, the web portal enables interactive query
to search for specific entries based
on various epidemiologic fields, for example,
defining specific geographic location at multiple levels,
like continent, country, or even down to the state, province,
or region, as well as a range of collection dates.
Queries can also be performed using a few viral genetic
characteristics, for example, the presence
of specific amino acid substitutions
within the spike protein or assignment
to known variant classifications.
Each of these search criteria can be used individually
or in combination.
Selected entries that meet the search criteria can be analyzed
directly on the GISAID website using CoVsurver, an application
that highlights interesting candidate amino acid changes
for further experimental analyses.
Or entries can be downloaded
for local analyses using any preferred software tool,
like MicrobeTrace or a local installation of Nextstrain.
A few format options are offered
when downloading selected entries.
One can download the consensus viral genome sequences
in standard FASTA format for analyses with a variety
of phylogenetic software applications
or download a tab-separated metadata table
with specimen information and either patient data
or technical information about the sequencing methods.
You can also extract a list of GISAID accession IDs
from these metadata tables and share it
with your bioinformatics staff to request they be included
in something like a local Nextstrain build, for example.
Whichever format you prefer when downloading these data,
be mindful of the terms of use outlined
in the GISAID's Database Access Agreement,
which stipulates how the data can be used while appropriately
acknowledging the submitters.
A link to the terms of use is included here.
Alternatively, individual records can be retrieved using
the accession ID, sometimes referred
to as the ISL accession or just ISL number.
For example, perhaps while analyzing sequences
from your laboratory using a tool like UShER,
you've identified a closely-related public sequence
stored in the EpiCoV database.
Query with the accession ID allows access
to detailed information about the viral genome sequence,
patient metadata, sample information, and the submitter.
The sequence and metadata in the entry can be downloaded.
And there's also a web form
to contact the submitter directly via email.
Again, keep in mind the GISAID terms of use
when accessing or using these data.
Now let's turn to NCBI, which hosts a breadth of resources
for biomedical and other scientific disciplines.
This module will only highlight a few features regarding storage
of microbial pathogen sequence data, including for SARS-CoV-2,
which are organized
in the simplified database hierarchy illustrated
by this schematic on the left.
Note that there are three main layers to this organization.
At the top, BioProject records, as the name implies,
describe research projects or study goals
and usually something about the included data types
or sample scope.
Occasionally, multiple BioProjects are collected
under what's known as an umbrella project,
and a link to the umbrella project
for SARS-CoV-2 sequencing conducted by laboratories
in the US is included on the toolkit web page.
Below that, BioSample entries contain descriptions
of the physical biological specimen, the source material
from which the data are derived, and any metadata.
These can describe primary specimens like nasal swabs
or even purified pathogen cultures.
Multiple BioSample entries are often linked
to a common BioProject.
Finally, the actual sequence data reside at the bottom,
and because multiple data types can be generated
from a single specimen or sample,
many data entries can link to a single BioSample record.
Discrete data types are stored in separate databases.
Within the scope of this module,
the most relevant databases are GenBank and Nucleotide,
which store consensus genome assemblies similar to GISAID.
NCBI also stores raw sequence read data
in what's called the sequence read archive, abbreviated SRA,
that are valuable for analyses outside the scope
of this module.
Just note that it's standard practice
in many laboratories conducting microbial pathogen sequencing
to submit both the consensus genome assembly
and raw sequencing data for every specimen.
Notice how this organization differs from GISAID,
which stores the metadata
and assembled genome sequences together as one entry
in the EpiCoV database.
That simplicity benefits from GISAID's specialization,
whereas NCBI accommodates a wider range of data types
and biological sources,
flexibility that demands more complex organization.
Thankfully, NCBI does well to hide some of that complexity
when searching the sequence databases and the collection
of SARS-CoV-2 resources are all accessible via the link here,
which is also included on the toolkit webpage.
From there, it's possible
to access all SARS-CoV-2 assembled genome sequences
and raw sequencing reads even if they are spread
across multiple combinations of BioProject
and BioSample records.
NCBI also provides an interactive dashboard to search
for SARS-CoV-2 consensus genome assembly records
at the link provided here, and also on the toolkit webpage.
For example, interactive searches based on geography
and time are made by simply clicking
to highlight an individual state, or even multiple states,
or by defining a range of collection dates at the bottom.
Notice that these filters are applied
to the data summary counts listed
in the blue box at the top.
And when clicking the button to view and download results,
the data listed are already filtered according
to these criteria.
Here's the list of sequences captured by the geographic
and collection date filters from the previous slide,
which are indicated on the left side: Georgia, USA,
and dates January 20th through March 2nd, 2021.
From here, the collection can be further refined according
to more search criteria on the left,
which include both technical metrics, like sequence length,
as well as sample metadata.
Selected sequences can then be analyzed further right
in the browser with either an annotated multiple sequence
alignment or a simple phylogenetic tree.
Note that both provide a quick comparison
of the selected sequences but may not be ideal inputs
for further downstream analysis.
The selection can also be downloaded for analyses
in other preferred software tools.
Like GISAID, NCBI allows downloading the selected records
in a few different formats.
As before, download the consensus viral genome sequence
in standard FASTA format for analyses
with many phylogenetic software applications.
NCBI also allows downloading only the annotated gene
or predicted protein sequences, also in FASTA format.
Or download a list of selected accession numbers to share
with your bioinformatics colleagues
to request they be included in some other local analyses.
Again, or you can download a comma-separated metadata table
with specimen information, similar to what's shown
in the table used for making the selection.
Knowing how to search public genome repositories
and download relevant SARS-CoV-2 sequences can provide valuable
context to analyses of local data generated
in individual public health laboratories.
This can be particularly useful
for laboratories just getting started
with only a few sequences or in jurisdictions
where sequencing is conducted by multiple institutions,
including academic laboratories.
Consensus genome assemblies in FASTA format
from different sources can be combined
for analysis using most phylogenetic software tools,
including Nextstrain, MicrobeTrace, and UShER,
which are highlighted in other modules
of this toolkit listed here.
In summary, collecting
and openly sharing SARS-CoV-2 genome sequence data ensures the
greatest benefit to both local
and national public health efforts.
These sequence data are primarily organized
in two popular repositories that can be efficiently queried
to supplement local analyses,
as well as support large open source projects
to track virus evolution, such as Nextstrain and Pangolin.
Therefore, depositing sequence data and associated metadata
into public repositories should be an integral step
in the SARS-CoV-2 sequencing workflow of any laboratory.
While the details of submission are outside the scope
of this module, some links to helpful resources are provided
on the toolkit webpage, including information
about SARS-CoV-2 contextual metadata standards
from the Public Health Alliance
for Genomic Epidemiology, also known as PHA4GE.
This concludes Module 3.5.
Part 3 of this toolkit focuses on other useful tools and skills
to apply genomic data, and in particular, to integrate
that data with the epidemiologic or clinical data.
The three modules listed here include details
for analyzing sequence data that can be downloaded
from public repositories highlighted
in the current module.
Please visit the COVID-19 Genomic Epidemiology Toolkit
page where you can find further reading on this 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.