 My name is Vivian Bonazzi. I'm a Program Director for Bioinformatics at Extramural NHGRI. I'm Elliot Margulies and I'm a basic researcher. My name is Jeff Schloss. I'm a Program Director in the Division for Extramural Research at NHGRI. My name is David McGoy 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. We were hearing about this human genome project at that time. This was before I was at NIH obviously. I think 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% 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% of the genome at reasonably high draft quality. Those are worldwide figures. Having the sequence to 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. I mean, nothing that I do could be done without a genome. I mean, it's core research. Today, we could 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 the top part, and we have web browsers that house the genome and databases. I absolutely do take it for granted. I mean, 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. And so it would be a shock if I was to work on an organism without a sequence genome, I think. So 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 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 the 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 at many genomes and just find out what's going on. The big issue is that what we've got now is we're trying to figure out what that data means. So you have to have the data in a format that you can actually use. So we have to have, otherwise, it's a tower of Babel. 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 Finley, and I'm a basic researcher in the lab of Dr. Julie Segre. My name is Sean 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 two billion of them every second. We generally use zebrafish 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 copydom 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 on our skin. The Brody lab is interested in one-carbon metabolism. What carbon metabolism is a set of pathways which are important in underlying development, and they relate to 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 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 phenotype? I love genetics, and like I said, that's my favorite way to tackle 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. What we've done is try to see if we can sort of focus in on this one exposure, house-dye splite 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 mite and basically see which mice respond to which don't, and then correlate that with the genetics of each of those mice. They're genomes, essentially. We know that malassezia, which is a fungi, is 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? And so if there are, are these fungi 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 look 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 roadmap 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 others that have been sequenced. And oftentimes, human is our closest match. Well, I think we're kind of moving with our results from 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 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 sequenced. And so personalized medicine will be real and not just something we're talking about right now.