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My name is Vivian Bonazzi. I’m a program director for bioinformatics at extramural
NHGRI.
I am Elliott Margulies and I’m a basic researcher.
My name is Jeff Schloss. I am a program director in the Division of Extramural Research at
NHGRI.
My name is David McGaughery and I’m a basic researcher.
My name is Eric Green. I’m the Director of the National Human Genome Research Institute
and I would consider myself both a basic science researcher and a clinical researcher.
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We were hearing about this human genome project at that time. This was before I was at NIH,
obviously. Many of us thought that that was an unbelievable dream probably very far off
into the future.
I’ve never really experienced not having a genome. I mean, scientists of my era don’t
know what it’s like, which maybe is a good thing because whenever we need to look up
a gene we can type in a name, a gene pops up, we have the sequence information, we have
expression information.
In 1999, we had over the past several years, mostly over the past three years, accumulated
data on about one tenth of the human genome.
We had obtained about 2.4 gigabases of raw data from the human genome. That was enough
to put together about 10 percent of the human genome sequence. The plan was for the subsequent
year, to put together about 16 gigabases of human genome sequence data, and that would
be enough to sequence about 90 percent of the genome at reasonably high draft quality.
Those are worldwide figures.
Having sequencing the human genome is like turning on the lights in the room and finally
knowing what you’re looking at.
If we jump forward to 2004, you could sequence one human genome in about three months with
100 machines.
Having nothing that I do could be done without a genome. I mean, it’s core to research.
Today, we can sequence the same amount of DNA that it took three months with 100 machines
in 2004; with the current technology we can sequence that amount of DNA in about one day
on one machine.
In terms of warp speed, with the volume of data increasing exponentially, then the need
to be able to figure out all of the processes for informatics just exponentially goes up.
So our need for our knowledge of computer science to be able to handle this information
and to be able to build appropriate, scalable tools, just increases exponentially as well.
We have fancy, big monitors that we spread the reference sequence along, you know, the
top part and we have browsers, Web browsers that house the genome in databases.
I absolutely do take it for granted. I’m sure the older generation doesn’t, but I
certainly take it for granted because I’ve always had it available to me. It’s just
a given. So it would be a shock if I was to work on an organism without a sequence genome,
I think.
My work here at the NHGRI is to really leverage the next generation sequencing technologies
and apply them and see what we can do with them.
Oh, man, we could not -- we certainly could not have done this 10 years ago. One thing
that’s really been enabling our project here is the acquisition of next-gen sequencing,
and these machines, especially the platforms that we use today, are only a few years old.
In fact, the acceleration of sequence acquisition is astounding.
These next-generation sequencing technologies are fantastic. The amount of human, or any
really kind of sequence that comes out of machines is fantastic. Basically we can use
it to detect variation, to detect breaks in the DNA, really just to examine not only one
genome but really many genomes and just to find out what’s going on.
The issue is that what we’ve got now is we’re trying to figure out what that data
means. So we have to have the data in a format that you can actually use. So we have to have,
otherwise it’s the tower of Babel.
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I'm Dave Bodine. I work in the genetics and molecular biology branch and I would probably
describe myself as a translational researcher. I try to stay in between the very basic things
and some of the more clinical things.
My name is Keisha Findley and I’m a basic researcher in the lab of Dr. Julie Segre.
My name is Shawn Burgess. I'm a basic researcher.
My name is Heidi Parker and I'm a basic researcher.
We study hematopoiesis which is the production of blood cells from a very undifferentiated
stem cell in the bone marrow and we're particularly interested in how that stem cell makes genetic
decisions to differentiate into red blood cells which you make about 2 billion of them
every second.
We generally use zebra fish as a model organism where we can study and experiment on these
fish to study the function of genes.
The research I do here is to study copy number variation. This is a type of large-scale genetic
variation that affects large deletions and amplifications of genetic sequence in our
genome.
What I’m studying in the lab is focused on whether or not fungi are found in our skin.
The Brody Lab is interested in one carbon metabolism and that’s -- one carbon metabolism
is a set of pathways which are important to underlying development and they relate to,
like, dietary vitamins.
I study dog genomics. So, what we do is map all sorts of traits in dogs, either morphologic
traits, disease traits, and sometimes even behavioral traits. And by what we do is we
use a lot of information gained from the genome sequence in order to do that.
The kind of research I do, basically aims to understand disease as a function of both
genetic risk factors and environmental risk factors.
The question that we’ve been working on at the NHGRI is to try to understand how does
genomic variation affect a phenotype.
I love genetics. And like I said that's my favorite way to attack a problem because geneticists
are the, in my mind, the vanguards for understanding. They're the ones that get the first look into
how a gene works.
We started getting into dogs was because dogs get a lot of different diseases, a lot of
inherited diseases, and they’re the same diseases that humans get.
So what we’ve done is try to see if we can focus in on this one exposure, house dust
mite exposure, and apply that to an animal model, where we can then survey their genome,
instead of the human genome.
So what we do is essentially we have a population of mice that you can think of as a population
of humans, and we expose them to dust mites and basically see which mice respond and which
don’t, and then correlate that with the genetics of each of those mice, their genomes
essentially.
We know that malassezia, which is a fungi found on human skin on your face and your
scalp causes dandruff, et cetera. But the question is, are there other fungi present
on our skin? So if there are, are these fungi at the healthy -- in a healthy individual,
can you see the fungi on your skin and determine whether or not they have -- do they play a
role, normal role on your skin? And then if we look at disease, skin disorders, are the
fungi also responsible for causing these disorders?
Well, a lot of the tools we use are actually computational tools, so the data sets that
we’re using now are huge data sets and we’re looking for big changes, so some of what we’ve
been using are these large-scale sequencing machines, these next-gen technologies.
The basic question is to identify the genes that control traits that are important, not
only in dogs, but in humans as well. So, that always leads us to disease. We looked at a
lot of different cancers in dogs.
They’re showing us new biology. How do we connect the dots, really?
The human genome was really a gateway genome that allowed other vertebrate model systems
that were used to study human diseases. They gave them a road map of how to do a genome,
how to do it.
It's pretty easy to go back and forth between the two. And when we line them up, we'll look
at dog and mouse and human and some of the other, that have been sequenced and, oftentimes,
human is our closest match.
Well, I think we're kind of moving with our results from the basic to the clinical. It's
just the nature of the work that we've been doing. We've kind of evolved into the position
that we are in now.
In 10, 20 years, I really feel like science will continue to grow and to progress and
we’ll see that most people are probably going to start getting their genome sequence
and so personalized medicine will be real and not just something we’re talking about
right now.
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