Tanja Davidsen: Good afternoon, everyone.
[inaudible] -- okay, great.
So, I will be talking about the NCI cancer genomics cloud pilots.
But before I do that, I’m going to also talk about the Genomic Data Commons.
So, we’ll start with that.
So, the Center for Cancer Genomics, or the CCG, has three major projects in cancer genomics:
TCGA; TARGET, which is the childhood cancer genomics projects; and the CGCI, Cancer Genome
Characterization Initiative.
And is this -- okay.
Yeah, okay.
So, they’re represented here.
All three of these projects -- each have their own data coordinating centers, as well as
their own data portals.
So, you have to go to three different data portals to get the data on these three projects.
In addition, for the sequencing data, the BAM files for all three projects are stored
in a second location, not the data coordinating center.
You will not be able to get that data through the data portal.
You would either need to, for TCGA, TARGET, and CGCI, come to the Santa Cruz site CGhub
to get your BAMs.
The TARGET and CGCI BAMs are also available at the NCBI sequence read archive, SRA.
But if you want the higher-level data, while the BAMs are at CGhub or SRA, the VCFs and
MAFs are, again, at the DCC.
So, this is -- this is kind of confusing for users, I think [laughs], to have all of this
data, all generated from the same CCG -- from the same division, CCG -- to have it all in
so many different locations.
So, the goal of our Genomics Data Commons, or the GDC, which is currently in production,
is to unify this fragmentary repository at NCI so that all of the cancer genomics projects
that we have at the CCG -- and in the future, hopefully, all the projects we have at the
NCI -- will be available from a single repository.
There’ll be a single sign-on for all users.
You won’t have to go to multiple places to get the data from multiple projects.
Now, a secondary goal of the GDC is to harmonize the diverse standards that we also see in
our data.
So, here on the left-hand -- oops, sorry -- here on the left-hand side of the slide, we have
-- it just -- the representation of the BAMs being aligned to various different references.
So, are you HG18, are you HG19, are you HG20?
This causes a lot of difficulty in analysis, of course.
On the right-hand side, we have a representation of the Venn diagram for mutation callers with
very little overlap.
Again, if you’ve got your mutation calling from two different pipelines that don’t
overlap very much, again, it’s going to make it difficult for analyses.
So, what -- a secondary goal of the Genomic Data Commons is to go ahead and harmonize
some of these data standards.
So, BAMs will all -- all of the BAMs that go into the GDC will be aligned to one reference.
And they will all also get the same mutation-calling pipeline.
So, the Genomic Data Commons is a contract.
It was awarded to the University of Chicago, and the P.I. is Dr. Robert Grossman.
The go-live date for full functionality for the GDC should be around late spring of 2016,
so that’s when you can anticipate being able to log onto one location to get all of
your data.
And finally, it’s not a commercial cloud.
The photographs here represent the actual data warehouse that’s at the University
of Chicago.
And one of the benefits of not being a commercial cloud for the storage of this data is that
it will be free to download the data.
So, speaking of clouds, I’m going to move on now to talk a little bit about the cloud
pilots.
Right now, the standard model of computational analysis is, of course, you’ve generated
your local data.
You keep it locally stored.
You -- if you’re interested in TCGA data or other public data sets, you have to go
ahead and down that -- download that locally as well.
You’ve got -- you’ve got your locally developed software and then any software that
you pulled down from other locations.
Everything, again, is local to your university or research lab.
And as a user, you’re doing everything locally.
And this works fine until the data sets get quite large.
So, there are definitely some limitations to the standard model when you’re dealing
much larger data sets.
So, assuming that we have approximately 2.5 petabytes of TCGA data towards the end of
the project, the storage and data protection will cost about $2 million a year.
Downloading the TCGA data at 10 gigabits per second, which is really a best-case scenario,
is going to take a minimum of 23 days.
So, you can probably guess more like two or three months to download all this data.
And at this point, really only large institutions have the ability to utilize all this.
We -- and moreover, these data types are going to continue to grow, right?
We’re going to have new projects after TCGA is closed, new projects at NCI, other projects
-- for example, TARGET and CGCI.
So, we anticipate that the data sets will just get larger and larger.
Okay, this slide does not look like it should.
All right; that’s okay.
So, the answer, of course, is to co-locate all of the compute and data in the cloud.
And my cloud is very fractured, you see here.
It used to be like this.
[laughs] I don’t know what happened.
All right, so -- but, in any case, what we want to do is, we want to have everything
on the cloud.
We want to have our core data set; in this case we’re just talking about TCGA, at this
point.
We want to have user-uploaded data.
So, instead of -- instead of storing all your data locally, you would upload it to the cloud,
which should be a much easier task than downloading an entire TCGA data set.
You would also, of course, have some standard tools that would be available in the cloud,
as well as some user-uploaded tools.
And all of this computational capacity would be here in the cloud.
And as the user, you would come to the cloud, you would do all of your analysis, and you
would then, at the end, download only your results, which, again, should be a much smaller
data set than the original large data set including TCGA.
So, here you can see the data represented -- the data generated from a project like
TCGA going into the data coordinating center, where there’s QA/QC validation, as well
as aggregation of the data.
And once that’s done, we have the authoritative NCI data reference set.
This data set is being loaded into the Genomic Data Commons.
It’s just one of the reasons I mentioned that at the beginning of our -- of our talk
here.
So, all of this data is coming into the GDC.
This is going to be the main repository for all of this data.
The data is also being copied to our NCI cloud pilots.
Again, they’re fractured here [laughs]; sorry about that.
So, the data set will be -- will be replicated in the NCI cloud pilots.
Now, why is it replicated?
So, here you are as the user.
There is really one -- there’s different reasons to use the different tools.
So, you would come to the Genomic Data Commons or the GDC if you were interested in searching,
retrieving, or just downloading the data because this is the authoritative data reference set.
However, if you were interested in doing high-performance computing or analysis, instead of coming to
the GDC, you would come to the NCI cloud pilots.
And you’d be able to upload your user data.
You’d be able to upload your own analyses and then run everything on the clouds.
So, as I mentioned earlier, it’s really only the largest institutions right now that
can download, you know, three petabytes’ worth of data and run analyses on them.
So, this is NCI’s effort to democratize access to this genomics data.
It’s managed through CBIIT, where I’m from, in partnership with the CCG, and we’re
coordinating closely with the Genomic Data Commons.
We’ve awarded three contracts, one to Broad, one to Institute for Systems Biology, and
one to Seven Bridges Genomics.
Our period of performance started in September of 2014 and it goes to September of 2016.
If you’re interested, this link takes you to a CBIIT page -- information page on the
cloud pilots.
And our anticipated launch date is actually January of 2016.
So, fairly shortly, you’ll be able to start analyzing data using this.
So, some considerations for the cloud pilots -- first, the designs must be released under
a non-viral, open source license, so everything’s open.
For extensibility, the initial clouds need to focus on a set of core datatypes that I’ll
mention in a few moments.
But they also must extend to additional datatypes without major refactoring of the existing
system because we know that there will always be new datatypes that’ll come up.
As far as sustainability, we have cost assessments for operating at the current scale, which
is on the order of 2.5 petabytes, and also at 10 and a hundred-fold increases in storage,
compute, and usage.
So, we have an idea of what it would actually take to get to the next level once that data
is such -- so large.
And then, finally, for security, these systems need to be FISMA moderate.
All of the cloud providers need to be FedRAMPed.
And the three cloud pilots must all obtain trusted partnership, and this is an NIH trusted
partnership agreement which allows the cloud pilots or any other trusted partner to share
controlled-access data -- so, in this case, controlled-access TCGA data.
Finally, we have the open-access data versus the controlled-access.
With open-access data, everyone will be able to use and analyze at the cloud pilots.
For example, some of the open-access data would even include CCLE data so that you could
-- so that you could work -- do some analyses on BAMs if you do not have controlled-access
dbGaP approval.
However, if you have controlled access, you’ll be able to utilize all the data.
Okay, core data sets -- so, all three awardees have to host a common set of core data that
includes all DNAseq BAMs, RNAseq FASTQ and BAMs, SNP array cell files, somatic and germline
mutation calls -- so that’ll be your VCF files and your MAF files -- and all -- will
also have the clinical data.
In addition to that, each awardees was required to have at least one additional TCGA data
set, and they all chose to do more than one.
Broad chose to add validation BAMs, miRNAseq, and methyl-seq.
ISB is adding miRNA-seq as well as all level-three data across all of TCGA.
Seven Bridges has whole-genome and -exome DNAseq FASTQs, mriRNAseq data, and methyl-seq.
Okay, so, the project schedule and deliverables -- we have, just about a month ago, finished
up our design/build Phase I. That was six months of initial design and development.
We’ve just started design/build Phase II, nine months of completing the design, development,
and implementation.
And then, right around January of 2016, again, we’ll be in our nine-month evaluation phase.
And this is where we’re going to need the community members’ help.
So, we’re going to be opening up the cloud pilots to allow the community to evaluate.
NCI will also be evaluating the cloud pilots.
But we really need researchers in the community to try these systems out to tell us what is
working.
So, what are some things that are common to all three cloud pilots?
We have core -- the core data sets, as I mentioned.
Some use cases that everyone has to have are running preloaded pipelines on TCGA data;
uploading and processing, again, that user data that you can upload your own data; uploading
and running custom algorithms so you’re not forced to use what’s available on the
cloud pilot; you can upload any algorithm that you choose.
And finally, these pilots need to serve both the biologists as well as the bioinformaticians.
The Common Workflow Language is being considered as a workflow language for all three pilots.
That’s not finalized yet, but we’re looking at that.
All three will be using Docker containers for improved portability and reproducibility
and all three will be using the emerging GA4GH standards.
Finally, they’ll all have the same authorization and authentication process.
This is, again, to make sure that only people with controlled-access dbGaP approval get
access to things like BAMs.
Okay, so, I mentioned what the three have in common, but what makes them unique?
We chose three cloud pilots because we wanted three different solutions.
We want to see what works.
The first is the Broad cloud pilot, led by Gaddy Getz.
His collaborators are at University of California Berkeley, as well as Santa Cruz.
They have chosen to use Google as their cloud platform.
And some unique technologies they’re using include the ADAM and Spark, and they’ve
incorporated Firehose.
And they’ve chosen to call their cloud pilot FireCloud.
So, each of the three cloud pilots have their own website, which I’ll be showing on these
slides, as well as the very end slide today, if you’re taking notes.
Each of them have a link that allows you to go and sign up for notifications and news
on what’s going on and the opportunity to evaluate the system once it is open.
Okay, here’s the FireCloud slide.
So, FireCloud allows you to take your data, your tools, and your workflows and then put
them all in either a user or a teamwork space where you can securely track and manage your
data, metadata tools, job execution, and results.
And it also allows you to capture the provenance for each run.
There’s versatile job executers, as I mentioned earlier: Docker, ADAM/Spark, and Google Cloud
Dataflow.
And the data can be stored in these -- in these distributed stores called ReadStore
and VariantStore.
From ISB we have -- the P.I. is Ilya Shmulevich.
His collaborators are Google and SRA International, so of course his platform is Google.
He’s using the Google Genomics Platform as his -- one of his unique technologies.
And some of the tools they’ve incorporated include Regulome explorer and Gene Spot.
And ISB is really focusing on the interactive data visualization, exploration, and analysis.
And again, the cloud pilot website here is cgc.systemsbiology.net.
And I’ll be, again, repeating those.
Again, this is just a slide showing some of the highlights from the ISB cloud pilot.
The interactive tools allow you to explore all tumors or a subset of tumors, allow you
to define custom cohorts, focus on specific molecular datatypes or platforms.
Finally, we have programmatic access, including REST APIs for cloud storage, SQL-like queries
for BigQuery, and the GA4GH API for Google Genomics.
All three cloud pilots will be having tutorials and ways to learn more about the system and
how to use it.
Finally, we have the Seven Bridges cloud pilot.
The PI is Deniz Kural.
There are no collaborators for this -- for this contract.
But they, unlike the other two systems, are using Amazon Web Services.
Their unique technology includes the Seven Bridges Genomics platform.
And they’ve incorporated already over 30 public pipelines that you can -- that you
can see here at this URL.
And finally, their cloud pilot website is sbgenomics.com/cancer-genomics-cloud.
And here’s their last -- here’s their slide.
So, at the Seven Bridges cancer genomics cloud, you’ll be able to wrap your tools, mix your
data and match it with other data, including the TCGA, analyze the data, and then finally
collaborate and discuss what you’ve discovered with your colleagues through the Seven Bridges
platform.
Okay, so, I’m out of time.
But we also have a cloud workshop -- cloud pilot workshop this evening for additional
details.
All three P.I.s are here to be -- to talk about their specific pilots.
It will be from 4:00 to 5:00 and repeated again from 5:00 to 6:00 right here in Natcher
Auditorium.
So, I encourage you to please attend if you are interested in learning more.
And then, finally, we just have the project teams that make all this possible.
And once again, I’ve repeated the URLs.
Thank you.
[applause]
Male Speaker: How do you ensure that the economics after
two years would be affordable to most of the users?
Tanja Davidsen: That’s a good question.
So, one of the -- one of the things the cloud pilots need to -- need to deal with in the
next nine months is how to -- how to allocate use time for the evaluators, right?
So, right now, we’re really just looking at the evaluation phase and how to fund that
so that the evaluators do not have to -- do not have to put money into the system.
But -- so, I think we need to see what happens in the evaluation phase before we decide what
happens outside of the two years.
Male Speaker: Yeah.
Would you think that it will benefit here to bring in more commercial cloud provider,
three out of the two is Google?
So, there are other, you know, provider.
I think it would be great to have a competition so that they can, you know, make it more competitive
in terms of, you know, overall.
Tanja Davidsen: Yes.
I believe -- I believe the systems will actually be able to run on other cloud providers if
necessary.
Yes.
Male Speaker: Thank you.
Male Speaker: Just a quick follow-up, and maybe you don’t
-- you can’t answer this now.
But you just said, “Well, let’s see what happens in the pilot phase to see how that
carries forward.”
But so -- is there a tentative plan as to what would happen in the spring of 2016 in
terms of people getting access, free of charge, to those clouds to run their jobs --
Tanja Davidsen: Yeah, so --
Male Speaker: -- or will there be a limited capacity, or
what’s the rough plan, without commitment?
Tanja Davidsen: It’s -- it -- all three cloud pilots are
-- I think are going to come up with their own -- with their own system, but it will
-- everyone will -- everyone who wants to help with the evaluation will be given a certain
number of hours or credits or something like that to utilize.
And I think if you -- if you exceed your credits or you meet your credits, you could -- you
could ask for more credits.
Male Speaker: And the helpers with the evaluation will be
organized by your -- by your team?
Tanja Davidsen: Yes.
Male Speaker: Okay.
Tanja Davidsen: That’s right.
All right, thank you very much.
[applause]
Female Speaker: Thank you, Tanja.
So, our last speaker for this session is Andrew Gross.
He’s going to talk about paired tumor and normal analysis using TCGA data.
[end of transcript]