Brady Bernard: All right.
After that spectacular start, we're happy to be able to introduce GeneSpot, which is
an web-based portal for interactive exploration of TCGA data from a gene-centric point of
view.
I am Brady, and I'm joined by Hector.
I'm going to describe the motivation for this application, as well as the key features behind
it.
And then Hector will be able to go over a demo, hopefully, if it comes together.
And if not, then show some screenshots and discuss the implementation that we've taken
for this application.
All right.
So there are many cancer biologists out there who want to take their favorite gene and query
what the data are in the TCGA for that given gene.
And some example questions could very well be: Across all the tumor types, what does
a mutation profile look like?
Are there significant copy number aberrations?
What are the data-derived statistical associations?
What would plots of just the raw data look like?
Can they be downloaded?
And so on.
There's a lot of very gene-specific questions that can be asked across all these datasets.
Part of the issue at the moment is that many of the data repositories are organized in
a very tumor-specific or sample-centric point of view.
And so we're just trying to take an orthogonal view from those and provide a new way of looking
at the data.
Along those lines, a typical workflow to look at a gene-centric point for TCGA would be
to download the data from a repository, being DCC or Broad Firehose, for example, and then
parse and process all those data, and finally, put together all the features, being clinical,
molecular, whatever, across all the different samples and all the different tumors.
And then from that, be able to extract out the gene of interest, and then do the analyses
and create the plots, and move forward from there.
Part of the challenge is that it's actually not very trivial to do this.
The data sizes aren't that large, but the time to answer these types of questions can
take quite a while.
And so estimating, you know, 10,000 samples and about 500,000 data points for each of
those then, you know, gives you five billion data points, most of which aren't relevant
for whatever gene you're interested in evaluating.
So the approach we've taken is to create an Interactive Web Portal, which may or may not
work given the wireless, we'll see.
And then from that you can query a certain gene or sets of genes, and then start to explore
the data from there, without the need to download or install software.
The other part of the approach that we've taken is to provide basically a work space
or a controllable canvas where you can select different types of views given the gene that
you're interested in and what the data are telling you, and then move those, remove those,
expand, minimize, whatever you would like, and from those, create sessions that you can
then come back to at a later time, or share and collaborate with others to show what's
going on.
The other aspect, too, is to consider data direct access or provenance, where you want
to see what are the input files, what do tab de-limited data look like, how can I then
do my own subsequent analysis from those data, but querying them in a very accessible way.
A few example screenshots that will lead into the demo that Hector will have is not so much
to focus on the biology but just to give some quick snapshots of what you can look for.
And many of these views are inspired by others within TCGA community or typical views that
you would see when you're looking at any given gene across the different tumor types.
So here we have FBXW7 and can look at what its mutation profile is, and have also, in
the context, the different protein domains on the bottom and see where the mutations
are clustering, what types of mutations there are.
And the web portal, you can hover over many of these points and see what the details would
be, sample identifiers and whatnot.
Also, integrating in with other sources like MutSig rankings, where you can query and say,
"Here's my favorite gene."
What are the different ranks in different tumor types that can help prioritize subsequent
analyses that you might have to then drill down on and spend more time in the data portals
downloading data and whatnot.
Additionally, seeing the context of your gene within all the others within rankings; for
example, MutSig Top 20, which is taking a lot of the different tables that have been
created but with looking at them in a gene-centric point of view.
Also, copy number, primarily using -- just coming from Broad analyses, and looking at
which tumor types they're significantly amplified or deleted in, as well as inspired by the
oncoprints, looking at a per-sample view.
And again, this is just to show an example of how you can take different sets of genes
or individual genes and look at what they -- what their profiles are across different
tumor types throughout all the data.
Hector?
Hector Rovira: All right.
Let me switch to the other laptop real quick.
It's going to be interesting.
All right.
Okay.
Let me change the zoom level a little bit just for the presentation.
So as you can see -- you can see that okay?
Yeah.
So we're trying to -- one of the things that we're trying to do is create this interactive
environment that where you can bring in a lot of these different views together.
And so one of the main challenges, obviously, is getting all this information on the page
in a way that makes sense and that doesn't consume too much space on the screen.
I'll introduce some of the elements on the -- that you're seeing on the page.
At the top here, we're seeing all of the cancers that we load into the database, you can activate
the ones that -- or deactivate the ones that you want to show.
We provide like functionality, like moving the cancers around, and that affects the views
and how they present the data.
We also have, you know, loaded several genes here, and you can just type in, you know,
whatever gene of interest you have, and pull it up and add it to the list of genes.
And then, you know, do things like drag these around, or remove genes from the list.
And then each one of these elements, these -- one of these maps that we're calling here
has their own controls that provide information about, you know, just some basic controls
to minimize, refresh the view, move things, you know, move it around.
I'll go ahead and refresh that one, and....
And so we've been adding that elements to this as we go along.
Like Brady showed, the Top 20 is a good example of, you know, the Top 20 MutSig ranks across
all the genes.
And we're also showing like where we're getting this data from.
So, you know, just to have -- for our users, very informative.
Other things that we have is, you know, like these controls, this visualization offers
some controls in terms of, you know, how it's presented.
One of the things -- one of the key things that I was trying to achieve with this was
to be able to allow the users to control the layout and dynamically control the layout
of the visualization.
And so that they could prepare screenshots and show the data in action, you know, as
they're adding more genes to this -- to these panels, you might want to capture a screenshot,
you might want to see different information at different levels.
We also offer some controls on the left here that allow you also to control the zoom of
the panel if you have a lot of information and, you know, you want to move this around,
maybe you want to focus on particular parts of the applications; you might not be interested
in, say, copy number right now so you can just minimize that away, and it pulls out
these -- it pulls it back into this maps menu which shows you all the different views that
we're actually providing you.
Some of these are disabled right now, which they're in progress, we're still working on
them.
An example of that is this one that shows a kernel density plot and, you know, this
one has -- is kind of like in between so that's why we haven't fully released it.
But the idea is once you select a couple of genes that you'll be able to look through
all the features that are available for that gene, and then they get plotted, depending
on their data types, either a scatter plot or this type of plot.
Another -- and, yeah.
Brady Bernard: [inaudible]
Hector Rovira: Oh, yeah.
And so the last thing that I want to highlight, or a couple more things, is the ability to
maintain sessions, so you could save the state of what you're working on, maybe you're focused
on a particular cancer but you want to switch to a different context, and, you know, by
selecting one of these sessions, it'll activate different panels.
Yeah, there you go.
And then it'll pop out this information, and, you know, it tries to save the state of where
you were at, you know, where the different visualizations.
And finally, the last thing I want to highlight is that we've integrated with OWA [spelled
phonetically] security with Google Plus, and so the idea being that you can sign in with
your same credentials that you might use in a Google account, and we will store information
in relevance to that, and we will also try to integrate with other services, like Hangouts
or Google Drive, so you can take the information from here and save it out.
So that's future plans.
We also have links here to our website, and to the TCGA, and to other applications like
Regulome Explorer and Pubcrawl, which we have developed using the same principles and using
the same databases, but that they take a different view of the data so it's not gene-centric.
Regulome Explorer is application that looks at the associations that are derived from
the data, and Pubcrawl is looking at literature and merging that with the data that we have
from the TCGA so we can get a sense of what...
And then finally, going back to the slides -- how do you start from this slide?
Oh, yeah.
So this is just a quick diagram of the suffer [spelled phonetically] architecture behind
it.
We have a very, you know, this is the application, it's a HTML5 java script visualizations web
application.
We used a number of cloud services, as I've highlighted.
We present the data through these web services, very lightweight web services that can also
be used by computational biologists for programmatic access.
We offer -- we have this real-time analysis for all pairs associations, which we'll be
enabling soon.
And then we prepare these databases, these are NoSQL and graph databases that we load
all the data from TCGA, and right now, our data sources you can see at the bottom, we
take a lot of data from Firehouse, from the DCC, and from other GDACs as well.
And, you know, like I mentioned, some of the directions that we're taking is we would like
to add more views, more of these visualization that we would like to take from the work that
other GDACs are doing.
We think that everybody has a lot of good contributions to make and we could easily
integrate them into our application.
And, you know, we also have some other goals in mind.
And finally, we would like to acknowledge our group that helped put together this application
at the ISP, and our collaborators at MD Anderson, and also other collaborators in TCGA from
whom we've drawn data, and inspiration, and analysis.
Thank you.
[applause]
Lou Staudt: So we're going to have to go ahead -- we're
going to go ahead for the sake of time, but Hector and Brady will talk to you about their
cool application at the break.
And finally, we'll --