 I appreciate the chance to come and talk with you guys. So I think, as you heard from Eric, those of us that sort of been in the trenches for the last several years have this real shot to the head as to how much scale does matter. And I kind of want to talk about how we move this forward a bit, make it a bit more actionable to the improvement of human health, and talk specifically sort of about the role of technology in the process. So as you heard from Eric, scale drives discovery. And obviously, discovery drives opportunity for clinical translations. As we go through some of these large signature projects that NHGRI has made happen over the last two decades, really, we find more and more of these sort of actionable markers, mutations, what have you that give opportunity for move to the clinic. And it's crucial that technology be up to speed for this to happen. Technology, as it emerges, underlies both discovery as well as translation for human health. I think just two little things to point out here. When we talk about technology, we're not talking just about instruments and systems that one might buy off the shelf. This includes the development of infrastructure, methods, applications, software tools, and pipelines that support and go hand in hand with instrumentation. I should also point out that it's absolutely critical to get to the point where we've been able to achieve in some of these projects, that it is absolutely essential in terms of robust discovery platforms to run these technologies at production scale to achieve maximum impact. So if you just think about the big questions that have been asked by NHGRI at various times over the last 20 years at points when the technology was completely and totally insufficient to achieve the project goals. It goes back to the Genome Project, which marked its official start in 1989. I can speak as one of the people who got one of the first grants from what was then NCHDR. Jane Peterson was the program officer. And we were going to sequence 500 kb of human T-cell receptors. And I can remember just sitting in the reverse site visit and them asking, are you really going to be able to sequence 500 kb? And we kind of said, gulp. We think so. But we clearly didn't have the technology when we started. But we've made things happen. And in all of these other projects that I have listed, and code 1,000 genomes, the cancer genome atlas that you just heard Eric talk a little bit about, the microbiome project, these are projects. They were big ideas. We asked tough questions that were going to be important to health care when they got started. When we started TCGA and had initial discussions in 2005, the state of the art was PCR and Sanger sequencing. Now we start to think about projects, as you've just heard Eric mention a bit. What is the genomic basis of disease? What is the genomic basis of physiology? And how will we start to address these as well as clinical translation? And Richard will talk a little bit about clinical translation in a few minutes. Well, this slide just represents sort of the current commercial state of the art for large-scale genomics. Recently introduced, of course, was Illumina's new high-seq instrument. This is a nice evolutionary advance over the technology that we had previously. There's probably more headroom in there, but it will come out as the market allows. There are additional platforms that are or aren't really available or still have some dirty little secrets. The Pac-Bio instrument, for example, in our hands, 10 kb reads are a pretty common place. But the error rate is still a bit too high and the cost is still a bit too high to really operate this in places other than sort of niche applications. So we look forward to these platforms sort of continuing to push the state of the art and drive cost sound. We'll see how that goes over the next year or so. I wanted to give you a couple of examples, sort of emerging examples for how technology impacts not only scale but how it brings new applications to the forefront. The first place that I want to just touch on is how we might create a robust process for comprehensive clinical analysis of cancer genomes. So we use the term comprehensive in contrast to what you will see at many cancer centers and hospitals around the United States now. Quite often, a cancer patient can come through the cancer center door and be offered up some sort of genomic-based test. Typically, this involves looking at a small number of known cancer genes. Quite often, it's just genotyping-based. There will be a report. Sometimes there's reimbursement by the patient's insurance. But quite often, for many of these patients, there's just not any sort of actionable result. And one of the things that we and others have seen as we've started to pilot a very comprehensive suite of analysis that includes whole genome sequencing, exome sequencing, as well as a transcriptome analysis is starting to yield for many patients actionable results. As an example here, this is analysis of 17 patients who were seen in St. Louis with lung cancer. Each one of the columns in this matrix is an individual lung cancer patient. We've got 12 smokers over on the left and five nonsmokers over on the right. Each row on the left side indicates a gene that's been altered in these patients. And over on the right side of the matrix a corresponding targeted therapy that could be used once one of these gene alterations has been discovered. The different colored boxes show you the data set that these results were obtained from. Pink are SNVs, red are indels, purple are copy number variations, and then the green boxes are changes in gene expression that came from the RNA sequencing. The striking thing, obviously, is that every one of these patients has at least one actionable result. Several patients have many. And a physician can then decide to apply one of these targeted therapies. Again, the really striking thing is the number of actionable findings that come from the transcriptome analysis. This is something that is very rarely done at cancer centers in the US. I think there are probably only a couple of places that are even looking at RNA these days in a clear environment. This is sort of the holy grail of cancer genomics, right? You sequence a patient, you find a mutation, you've got an associated targeted therapy that you can use, terrific. But of course, many of these patients are treated and developed subsequently, drug resistance disease. This is sort of the next sort of stage of where we go with genomics technology. So for example, this is a patient who was diagnosed with melanoma, was treated with BRAF inhibitor successfully, but then several months later, his disease relapsed, the picture here on the right. And so a biopsy is taken from one of these metastatic tumors from these drug resistance patients. We can perform this comprehensive genome analysis. We identify through the DNA sequencing tumor-specific mutations. And again, it's not really here all about what's driving the tumor. But rather, can we use the technology to identify tumor-specific mutations that might drive immunotherapy. We utilize RNA capture here to identify those mutations that are expressed. And then using T cells that are taken from each of these patients, we can verify candidate immunoepidotes that elicit a response from the patient's own T cells and then infuse these back into the patients. So there are several clinical trials that are now underway with various approaches. The one at WashU uses this dendritic cell vaccine approach where we're basically loading the patient's own T cells with peptides that are generated from this SNV analysis and then infusing these back into the patients. Several patients in St. Louis have already received these vaccines. I want to switch quickly to one other exciting new application. So this is the use of some of these genomics technologies to look at individual cells for sequencing purposes. This is a paper that came out in November in Science by McConnell et al. Where neurons were isolated from human frontal lobe and essentially sorted out into a dish. And what you're looking at over here is the sequencing results from eight individual neurons. You can see that some of them are fairly quiet. But in quite a few others, there's a substantial amount of aneuploidy. These all appear to be relatively normal. And so the question is how much of this is going on? Obviously, this is just eight individual genome projects, if you will. And then there's a need, obviously, to better understand the physiology of the neurons to scale this up substantially. Likewise, a similar approach from the same group, looking at individual fibroblasts, these from an individual with a trisomy 21. You can see that the gain of chromosome 21 shows up in all of the samples that were sequenced here. Some other changes, it's possible to zoom in to sequence base resolution in several of these and find individual SNV variations from cells to cells. This has now been done by other groups as well, including Avi Rageg, who's now up to about 15,000 cells from several other different cell types. This is exciting and important application of the technology. It's going to drive our understanding not only of disease, but also the basic genomics of physiology. We don't yet understand all human cell types. We don't understand the changes that take place in a population. And so as we can start to apply techniques such as single cell genomics, we can start to get at the heart of the matter. Also in cancer, if you think about the heterogeneity that exists within a single solid tumor, maybe even from the core of the tumor to the outside of the tumor, and how that might affect the way a patient responds to one of these targeted therapies, this will be a crucial question to address. So just to conclude here, I've given you some examples of emerging technologies that really offer game-changing translational opportunities. And I think each one of these illustrates the enablement that comes by inexpensive, efficient genome sequencing and analysis that can be performed at very large scale. So I'll pass it to Richard to talk a little bit about clinical translation.