-: Today I'm gonna frame my talk
using buffalograss as a model,
and so that's the primary species that
our lab works with, so basically I'm gonna
chat a little about that.
Before I do, though, I think it's important
to revisit why buffalo,
or bio-informatics, there's too many "b" words in this talk,
so I'm gonna get confused.
But why bioinformatics is kind of emerging
as an important field.
And I'm gonna do that by looking at some
publicly available data.
I pulled some information that's available
from the National Center of Biotechnology Information,
which is a public repository.
Stores a lot of DNA sequence information,
along with lots of other information,
but for this talk right now I'm just gonna focus on
some of the expressed sequence tagged information
that they have.
So basically, what I'm showing here
is the amount of expressed sequences
that have been sequenced
and deposited in the database for wheat.
And so you can see that there's about
one and a half million, I think that's right.
Yeah, so about one and a half million
EST sequences for wheat.
That's a lot of data, right?
And so that's part of what we're talking about
for bioinformatics.
It's a recurrent theme that
came up with some of the talks this morning.
It's really a lot of data.
And also, to look at some of these other species
like soybean, it's about the same.
As for wheat, and then corn's up over two million.
But I don't have the luxury of working with those species,
unfortunately, so I work primarily on buffalograss.
As of last week when I pulled this data
there's zero ESTs published for buffalograss.
What I'm gonna do now is talk about some of the
challenges of using some of these tools
to explore, basically specialty crops
that don't have a lot of the same types
of resources as some of these bigger agronomic crops.
But again, to do that I think it's nice to
set the stage and give everybody a little background
on where, or how some of these tools have evolved.
And I know some of that discussion came out
this morning.
But this graph here I completely stole from the internet,
but basically it shows growth in
sequencing technology over the past 30 or so years.
And you'll notice,
on the horizontal axis this is showing years
since 1980 to the future, a future date.
And the vertical axis there is showing
the amount of sequence data that one of these
sequencers can generate, and so we're talking
really about analyzing and working with large
scale sequencing data sets.
And the other thing that it's kind of
important to point out is
you'll notice that the graph on the,
or the vertical axis is basically on a logarithmic scale.
So we've seen this huge growth
over recent years in particular,
with the amount of data that these
sequencers can generate.
So that's an important concept that we'll talk about.
And then, they're developing these sequencers
basically as fast as they can put one of these
graphs together,
so they're really coming in the market really quick.
One of my favorites is this little guy here.
It's called the MinION.
And it's no bigger than my cellphone,
but it has the ability to generate tons and tons of data
and it's basically just a USB key that you plug in,
you add your sample to it.
You're able to generate some data.
And since my kids are kind of like-minded,
they also do a lot of Google searching for
MinION, but of course they pronounce it
a little differently.
When we're talking about Big Data, I know Harkamal
touched on it this morning, as did Daniel
and some other folks, right.
This is just an example of some of the
sequence data that our lab generated.
Just to kinda give you an idea of how much
data we're really talkin' about.
We work with these horrible, non-graphical
terminal emulators when we're analyzing this data.
And so, on this particular slide here,
that screen capture is just showing
four sequences, which isn't much, right?
But this particular project, on this particular
data file had more than 12 million sequences,
just in one data file, right,
and so that'd be the equivalent of,
I think my math's about right.
So about three million screens of data.
That's a lot of information,
and it's just one text file,
and the text file itself is several gigabytes in size.
So, you can't even open these
in a lot of, you know, Microsoft Word,
or something like that.
So there's tons and tons of data.
And then, for this particular project
we had 36 such data files.
Harkamal talked about one that had, I think he said
800 or so.
Some of these projects get ridiculously big, and they're
generating ridiculous amounts of data, okay.
And so those are definitely some of the challenges.
A lot of the early drivers of this increase in technology,
so we're really talking about increase in technology,
we're able to generate a lot more sequence data
than we could five years ago,
10 years ago.
These are just a couple of projects that are driving
some of this innovation.
And honestly, there's hundreds or thousands more
of these types of projects.
You might imagine that we've seen
a lot of growth in the technology to generate this data,
but really the holdup is in bioinformatics
and computational biology.
There's a huge bottleneck in being able to
analyze the data,
and so that's becoming the problem.
And so now we're at a place where we can
generate as you saw on that previous slide,
or a couple of slides ago,
billions and billions of base pairs of sequence data
just in one sequencing run.
But the problem is, having the expertise,
the bioinformatics knowledge and
computational biology knowledge to be able to
analyze that data, and so that's really the challenge.
I kind of have a background in bioinformatics
and computational biology, so that's why
Roch asked me to talk about this today.
I do want to make one distinction, though.
We're kind of, I don't know that we have
any true bioinformatics people in the department.
So most of us that are on this side of things
would argue that we're computational biologists.
So, kind of a true definition
is that bioinformatics folks are the ones that are
developing new algorithms and software tools
to analyze some of the data, whereas
computational biologists are actually using the tools
to analyze the data sets, and so
I definitely fall more into that later category.
As I mentioned at the beginning, one of the things
I wanted to do is just talk about
some of our research in buffalograss
and how we can take advantage of some of these
modern sequencing tools to try and study
a plant system for which there's really no
up front genomic information, okay,
and so that's what I'm going to spend
a little time on.
And what I really want to do is just focus in on
just one project, we have several of these going,
but this is probly the one that's furthest along
in the process and I think is, we've have some
interesting data from it.
Leaf spot disease, it's caused by a disease complex.
I work with buffalograss, it's a low input, sustainable
turf grass species.
It's native to the Great Plains region.
Leaf spot disease is, again,
caused by several pathogens,
but it's one of the diseases that's important
for buffalograss.
Under the right conditions or, as a manager,
the bad conditions,
so under the right conditions you can get disease that's
severe enough to cause stand loss, and so that's a problem.
One of the things we did,
I also am a turf grass breeder, and so
we want to identify new sources of host resistance
to leaf spot disease.
And so I had a post doc, Sah-Jee Bah-Rom-Rah-Dah-Sah
that screened 84 different buffalo grass genotypes
in the green house, challenged 'em with
one of the pathogens that cause leaf spot disease
and as you can see on the right, we found
some that showed good resistance
to leaf spot disease in the green house
and others that were sensitive to it.
And so we picked some of those excessions
that were either resistant to the disease
or highly susceptible to the disease,
and in fact we picked two of each
and then we did one of these high-throughput
sequencing experiments.
We either grew those four plants, the two
resistant or two susceptible genotypes,
either under control conditions
or challenged them with the pathogen and then we
extracted RNA, we converted that to CDNA
and we sequence 'em.
And so that way we can basically look at,
take a snapshot and look at
all of the genes that are turned on
in response to the pathogen
at that one point in time.
So that's kind of the idea.
So what we're doing here is we're looking at,
basically 190,000 transcripts at one shot
and we're looking at how they differ between
the susceptible lines and the resistant lines.
And so one of the ways that we think is kinda creative,
one of the ways that we looked at this, again,
buffalograss doesn't have any up front information
and so, it limits what we can do a little bit with it.
So we turned to a good friend, foxtail millet,
and we mapped all the differentially expressed genes
to the foxtail millet genome.
And I think in this graph here the little red bars
and the blue bars show
where those differentially expressed genes
map to foxtail millet, and this shows the
nine chromosomes of foxtail millet there.
The length of the bars represent
the magnitude of change in gene expression
when we compare the control to the inoculated conditions.
And, admittedly this is a busy graph
and I don't expect you to take anything from this,
but basically we had a good distribution
of differentially expressed genes throughout
the foxtail millet genome.
And so if we just take a closer look
and look at chromosome eight, you might notice in that
six o'clock position there,
there are a handful of genes
that are turned on
in the two resistant lines shown in the red,
and then there are no genes in the same
position that are differentially expressed
in the susceptible lines, right, that are shown in the blue.
And so, to us that suggests that that's potentially
a genomic region of importance.
So even though we're using a Setaria italica,
this foxtail millet as a reference,
not buffalo grass, it gives us some insights
on genomic regions of potential importance.
So that's one of the ways that we're using
some of this data.
Another way is to do a traditional differential
gene expression type study, and so again I mentioned
that there are a hundred and I don't remember
how many thousand genes that we're looking at
under these conditions.
And in this graph we're looking at
the changes in expression, or comparing
the resistant lines to the susceptible lines.
So, Prestige on the bottom is
sensitive to leaf spot disease, so that's the
horizontal axis, anything on the right
is considered Up regulated in response to the pathogen.
And then, anything on the left is Down regulated
in response to the pathogen.
And then, 95-55 on the vertical axis is our
resistant line, so anything above that midpoint line
is being induced in response to the pathogen.
And, so you can imagine
a diagonal line there,
that's essentially showing that most of the genes
are behaving the same in both
the resistant and susceptible lines.
But the ones that really caught our eye
and that were most interesting to us are these ones
that are shown in that red circle there that were
Up regulated in response to the pathogen
in the resistant line, 95-55, and Down regulated
in Prestige, and so, we developed some
genetic markers.
We took a closer look and looked at the expression
of those, did kind of a typical heat map,
and so this is representing those 536 genes
in that little red circle and looking at
how they're expressed.
The red indicates that they're Down regulated,
green indicates that they're Up regulated.
And you can see that a lot of these genes
are Up regulated in the resistant lines,
Down regulated in the sensitive lines.
So that's kind of what we saw.
Doing these kinds of studies
and taking this kind of approach,
it gives us a lot of information
in buffalograss, and again we had
no up front information on buffalograss,
so in our eyes, using some of these modern
sequencing tools, it's pretty powerful.
So, again, can gain a lot of information.
Just kind of quickly here, we have several other
projects going on, looking at different traits
in buffalograss, we're looking at gender expression,
buffalograss is a diecious species,
so we're looking at
comparing male buffalograss to female buffalograss
and finding genes important for
gender expression.
We're looking at leaf spot disease resistance,
like I just showed, chinch bug resistance.
Just initiating a new project now where we're
looking at mechanisms of seed dormancy
and using some of these technologies to do that.
And then, again, I'm managing a buffalograss
breeding program, and so
my primary interest is in using all these tools
to develop genetic markers or learn some type
of information that we can apply to
improve the efficiency of our breeding program
to select, basically, either germplasm
that has new sources of resistance
or develop markers that we can track
resistance through our breeding population.
So that's kind of the idea.
Here's an example where we found some markers
that could do that.
These are gene expression based markers
that we used.
And again, the idea is then to be able to track those
and ultimately develop new buffalo grasses.
That's kind of the idea.
I promised Roch I would talk quick.
Try and get us back on track,
so that's all I have, so thanks.
(applause)