 So, I'm Mike Payson, and I'm going to go through the first half of these encode-like projects or other projects in functional genomics. And this is a really happy thing for me to be able to present because there's so much exciting work going on here. So it's an easy thing to do. It was also fun doing this because, as you heard from Dan's intro, we have a lot of contacts both at the administrator level and at the scientist level with these different projects. And it was reminded, this reminded me when I got in touch with some of my colleagues and said, do I have the information on your projects correct and just how helpful everybody is to work with? It makes an exciting time in this science area. So I'm going to start off by telling you very brief stories about a few projects, projects that are chosen because they're representative of what's going on science-wise. I'll start off with projects that are more similar to encode and then work my way towards projects that are more different from encode and then pass this on to Dan. I'm going to focus on consortium-type projects because that's the theme of what we're doing here, but I have to say I want to thank all of the individual investigator-initiated efforts, which have obviously played an important role in this area as well. And I'll start off by showing you a little bit of a timeline. Yeah, you can see with the pointer there, encode, of course, is purple, as we all know. And encode is a relatively long-running project among this group. But reference epigenome mapping centers, which I'll talk about first shown here in green, is a project that started during the encode cycle and has ended its data production. And one of the many important things I would say about REMC in addition to the very useful data they produced is they got this idea started, the International Human Epigenome Consortium. And they graciously refer to it starting at the time that the second project Blueprint joined on. And I'll talk a little bit about IHEP Blueprint and also Canadian project CHRC. So first I'll tell you a little bit about REMC, reference epigenome mapping centers. And this is funded by the NIH Common Fund. And the goal of these of the REMC projects, where did my pointer go, is to generate a public community resource, a common theme of these kinds of projects. And the way of doing this is to broadly sample tissues and organs across the human body, all right? And generally, a tissue or organ that's profiled by REMC has been done with many assays, sort of a grid-like layout. They had a publication package that came out just last month, or nature, a number of papers on biological findings, the resource, and how the data can be used. And I'd also like to point out the NIH Common Fund epigenomics program goes beyond the REMCs. It also includes disease-focused efforts, analysis projects, and technology development efforts as well. But here I'm presenting what REMC is up to. So REMC, like ENCODE, and many of these other projects is built on years of study of gene regulation. It's built on the foundation of mechanistic studies of how gene regulation works. The data production effort for REMC is complete at this time. And it has a high assay diversity. So it's one of those kinds of projects that does lots of different assays in the cell types of studies. And it also has high sample diversity. So there's a broad distribution with respect to the parts of the body which have been sampled, organs and tissues. But also of note, perhaps unique for REMC is that there are careful study of both fetal and adult human tissues. And the findings are quite different between the two. So it's important that they're both there. Next, I'd like to move to IHEC, an international consortium in some ways like ICGC is for TCGA. And the goal of IHEC is to understand the role of epigenome and human health disease and what are the influences of lifestyle, environment, aging, and genetics. And this is, of course, well aligned with thoughts about precision medicine and genomic medicine, understanding how we can use omics assays to learn about people's health and their health risks. And perhaps the effects of treatment. So goals here are to support prevention of disease, diagnosis, and treatment of disease. And because IHEC is focused on epigenomes, there's the chance to read out in real time, well not real time, but as a person's health status is changing, whether they're responding to a treatment or how their disease is progressing, rather than their one time over life genetic risk. Working groups in IHEC are shared across the projects that participate in IHEC, things like bioethics, assay standards, metadata standards, and data ecosystems. Before I started working at NHGRI, never appreciated how important metadata is, but now I get it, it's the thing that makes our data interoperable, it's the thing that allows me to use your data and you to use my data if I had any data. So we put a lot of effort into coordinating this cross projects, the very important thing. And IHEC has a standard set of data collection that's minimally required of projects, and it also has a data portal, where you can see data from all of the projects. So as a first example of an IHEC project, I'd like to bring to your attention Blueprint. And the goal is to generate epigenome resources using the clearly defined set of human samples, a mixture of disease and healthy tissues. So all projects in IHEC moving forward from Blueprint incorporate both healthy and diseased people to do comparisons, all right? And Blueprint is funded by the EU, and I really admire their logo here that has the two different, well. And the diseases that they're focusing on are a pair of cancers, lymphoma and leukemia, and also an autoimmune disease, type one diabetes. And in addition to collecting data, data analysis is a very important part of their work. They recently had a publication package in Science. And they're doing this work because they're very interested in discovering markers for disease which would support diagnosis. They're also interested in supporting epigenetic targets, new avenues for therapy. How could we treat disease knowing about the epigenomic signatures? I'd also like to talk about Canadian IHEC projects, CEHRC. The platform centers are part of the CEHRC as a whole. The overall project goal is to translate epigenomic discoveries into human health benefits. The platform centers are one part of that and they're located in Vancouver and Montreal. And they're primarily studying a few different cell types, blood, breast, brain and thyroid. And the particular disease of interest for their project is cancer. And I especially need to give a shout out to the Canadian project because they've taken the lead within IHEC on getting the data portal up and running, getting all of the data out there where it can be shared. And this benefits us all. And I'd point out that since a code is an associate member of IHEC, you can find some of the encoded data there as well. So if we look at IHEC, there's pretty high assay diversity, especially when you consider that most projects go beyond the minimum required by IHEC. And if you look at IHEC as a whole, there's high sample diversity. Across IHEC as a whole, many different body regions are sampled. Though it's not unusual that some of the projects focus on particular biological areas. And some of the IHEC projects are look at small numbers of individuals, so there's the potential to begin to find inter-individual differences. Next I'd like to talk about PsychinCode, which is funded by Benel Health, NIMH. And it's a consortium that includes 11 projects. And the goal is to identify non-coding functional genomic elements in human brain that are important in brain development and in brain disease. This is a large scale project, so they'll be looking at 1,000 brains and looking at different brain regions. And primarily this is to analyze psychiatric phenotypes. Schizophrenia, Alzheimer, bipolar disorder. So PsychinCode again has relatively high assay diversity. Different types of data are sampled. And it has a high potential to detect variation across individuals, with 1,000 brains being compared. It's not hundreds of thousands or millions cohort, but it's not one or two individuals either. There's a lot of potential to see natural genetic variation. And depending on how you think of it, there's a lot of biosample diversity within one particular organ, but not broad diversity across the body. So next I'd like to tell you a little bit about genomics of gene regulation. This is an NHGRI project. And this one is significantly different than the other projects that I've told you about. The goal here is to learn how to construct predictive gene regulatory network models. So these projects involve substantial data collection. But the point of this is not to generate data collections as a resource, rather to learn how to develop these models. All right, and a number of different biological systems are being used, including the adaptive and innate immune system and part of skin. And while many of the projects focus on transcriptional regulation networks, one focuses on post-transcriptional regulation. And the data from this project will be hosted at the ENCO DCC, as will the metadata, to try and make it user-searchable and make it more shareable for people in the outside community. So again, the goal is to learn these gene regulatory networks. And within each project, very often closely related cell fates or cell states are profiled, because in order to get at how these transitions occur, what goes in what network, it's the comparisons that are important rather than studying the broad part of the body plan. And while there's high assay diversity across these group of projects, we haven't forced them to all use the same assays, so you don't necessarily see the same assays across projects. So I'd like to talk about function of non-coding variants and predictive project, and I neglected to tell you that if you're in the audience in your folder, there's a spreadsheet or table, which has this information and URLs to some of this and so forth, which you could follow along or refer to after this meeting. But this project, again, has a quite different goal. And the goal is to learn how to computationally prioritize non-coding variants to know which are most important to follow up. And the reason for this is, as you've heard, an important NHGRI goal is to learn how to interpret the role of genetic variation in human disease and the vast majority of common variants lie outside of protein-coding regions. So if we throw out this large portion of data, it's going to undermine our power to understand all of the genetic component to human disease, all right? And genetic variation in non-coding regions is known to both cause and modify disease. I would point out that there are Mendelian diseases that are severe, like fragile X, for instance, that have long been known to be the result of non-coding mutations. Also more recent studies that have pointed for frontal temporal dementia and for ALS, again, non-coding mutations account for the largest fraction of known heritability. In that case, though, it's merely 30% of it. These awards are in process at NHGRI and our partner on this initiative, NCI. And the last project I'm going to tell you about is 4D Nucleome. This is brand new. In fact, so new that nothing is funded. The goals of this project are to understand the principles of nuclear organization. Why does the particular nuclear organization seen in a cell exist? And what does that organization tell the cell in terms of its cellular function, development, and perhaps disease? A lot of this work is going to focus on technology development, though some of the work will be devoted to reference maps and also predictive modeling of how the structure and function of the nucleus is correlated. So those of you that are familiar with this area know that historically there's been a lot of work done with imaging. More recently, there's been work that's done with genomics and for the most part, there's a vast gulf between the two. So one of the ideas that this project would like to do is to try and reconcile and bridge this gap. And this is again funded by the NIH Common Fund and awards could start as early as the end of this fiscal year in this project. So I will stop there and hand it off to Dan. Thanks.