Perspective
Perspectives on ENCODE
https://doi.org/10.1038/s41586-020-2449-8
Received: 7 December 2019
Accepted: 5 May 2020
Published online: 29 July 2020
Open access
~Sl Check for updates
The ENCODE Project Consortium*, Michael P. Snyder12S, Thomas R. Gingeras3,
Jill E. Moore4, Zhiping Weng4 5 6, Mark B. Gerstein7, Bing Ren8 9, Ross C. Hardison10,
John A. Stamatoyannopoulos11'12,13, Brenton R. Graveley14, Elise A. Feingold15,
Michael J. Pazin15, Michael Pagan15, Daniel A. Gilchrist15, Benjamin C. Hitz1, J. Michael Cherry1,
Bradley E. Bernstein16, Eric M. Mendenhall1718, Daniel R. Zerbino19, Adam Frankish19,
Paul Flicek19 & Richard M. Myers18
The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate c/s-regulatory elements (cCREs) that may serve functional roles in regulating gene expression1. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.
The ENCODE Project was launched in 2003, as the first nearly complete human genome sequence was reported2. At that time,our understanding of the human genome was limited. For example,although 5% of the genome was known to be under purifying selection in placental mammals3,4, our knowledge of specific elements, particularly with regards to non-protein codinggenes and regulatory regions, was restricted to a few well-studied loci25.
ENCODE commenced as an ambitious effort to comprehensively annotate the elements in the human genome, such as genes, control elements, and transcript isoforms, and was later expanded to annotate the genomes of several model organisms. Mapping assays identified biochemical activities and thus candidate regulatory elements.
Analyses of the human genome in ENCODE proceeded in successive phases (Extended Data Fig. 1). Phase I (2003-2007) interrogated a specified 1% of the human genome in order to evaluate emerging technologies6. Half of this 1% was in regions of high interest, and the other half was chosen to sample the range of genomic features (such as G+C content and genes). Microarray-based assays were used to map transcribed regions, open chromatin, and regions associated with transcription factors and histone modification in a wide variety of cell lines, and these assays began to reveal the basic organizational featuresof the human genome and transcriptome. Phase II (2007-2012) introduced sequencing-based technologies (for example, chromatin immunoprecipitation with sequencing (ChlP-seq) and RNA sequencing (RNA-seq)) that interrogated the whole human genome and transcriptome7. General assays such as transcript, open-chromatin and histone
modification mapping were used on a wide variety of cell lines, while more specific assays, such as mapping transcription factor binding regions, were performed extensively on a smaller number of cell lines to provide detailed annotations on, and to investigate the relationships of, many regulatory proteinsacross the genome. Transcriptome analysis of subcellular compartments (the nucleus, cytosol and subnuclear compartments) of these cells enabled the locations of transcripts to be analysed7.
ENCODE phase III
ENCODE 3 (2012-2017) expanded production and added new types of assays8 (Fig. 1, Extended Data Fig. 1), which revealed landscapes of RNA bindingand the 3Dorganization of chromatin via methods such as chromatin interaction analysis by paired-end tagging (ChlA-PET) and Hi-C chromosome conformation capture. Phases2and 3 delivered 9,239 experiments (7,495 in human and 1,744 in mouse) in more than 500 cell types and tissues, including mapping of transcribed regions and transcript isoforms, regionsof transcripts recognized by RNA-binding proteins, transcription factor binding regions, and regions that harbour specific histone modifications, open chromatin, and 3D chromatin interactions. The results of all of these experiments are available at the ENCODE portal (http://www.encodeproject.org). These efforts, combined with those of related projects and many other laboratories, have produced a greatly enhanced view of the human genome (Fig. 2), identifying20,225 protein-codingand 37,595 noncodinggenes
'Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA. cardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA. functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. 4University of Massachusetts Medical School, Program in Bioinformatics and Integrative Biology, Worcester, MA, USA. department of Thoracic Surgery, Clinical Translational Research Center, Shanghai Pulmonary Hospital, The School of Life Sciences and Technology, Tongji University, Shanghai, China. 6Bioinformatics Program, Boston University, Boston, MA, USA. 7Yale University, New Haven, CT, USA. BLudwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA. 9Center for Epigenomics, University of California, San Diego, La Jolla, CA, USA. ,cDepartment of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA. "Altius Institute for Biomedical Sciences, Seattle, WA, USA. ^Department of Genome Sciences, University of Washington, Seattle, WA, USA. ^Department of Medicine, University of Washington, Seattle, WA, USA. "Department of Genetics and Genome Sciences, Institute for Systems Genomics, UConn Health, Farmington, CT, USA. ,5National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. ,6Broad Institute and Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. "Biological Sciences, University of Alabama in Huntsville, Huntsville, AL, USA. ,BHudsonAlpha Institute for Biotechnology, Huntsville, AL, USA. ,9European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK. *An alphabetical list of authors and their affiliations appears online. A formatted list of authors appears in the Supplementary Information, ^e-mail: mpsnyder@stanford.edu
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2qqq   i □ 3D chromatin structure
' ■ DNA accessibility
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Cumulative number of ENCODE assays (human and mouse)
Fig. 11 ENCODE assays by year. Accumulations of assays over the three phases of ENCODE. 3D chromatin structure includes Chi A-PET (62 experiments), Hi-C (31), and chromatin conformation capture carbon copy (5C, 13). Chromatin accessibility includes DNAase-seq (524), assay for transposase-accessible chromatin using sequencing (ATAC-seq, 129), transcription activator-like effector nuclease (TALEN)-modified DNAase-seq (40), formaldehyde-assisted isolation of regulator elements with sequencing (FAIRE-seq, 37) and micrococcal nuclease digestion with deep sequencing (MNase-seq, 2). DNA methylation includes DN Ame arrays (259), WGBS (124), reduced-representation bisulfite sequencing (RRBS, 103), methylation-sensitive restriction enzyme sequencing (MRE-seq, 24) and methylated DNA immunoprecipitation coupled with next-generation sequencing (MeDIP-seq, 4). Histone modification includes ChlP-seq (1,605) on histone and modified histone targets. Knockdown transcription includes RNA-seq preceded by small interfering RN A (siRNA, 54), short hairpin RN A (shRNA, 531), clustered regularly interspaced short palindromic repeats (CRISPR, 50) or CRISPR interference (CRISPRi, 77). RN A binding includes enhanced cross-linking immunoprecipitation (eCLIP, 349), RNAbind-n-seq (158), RNA immunoprecipitation sequencing (RIP-seq, 158), RNA-binding protein immunoprecipitation-microarray prof iling (RIP-chip, 32), individual nucleotide-resolutionCLIP (iCLIP, 6) and Switchgear (2). Transcription includes RNA annotation and mapping of promoters for the analysis of gene expression (RAMPAGE, 155), cap analysis gene expression (CAGE, 78), RN Apaired-end tag (RNA-PET, 31), microRNA-seq (114), microRNA counts (114), more classical RNA-seq (900) and RNA-microarray (170), including 112 experiments at single-cell resolution. Transcription factor (TF) binding is ChlP-seq on non-histone targets (2,443). Other assays include genotyping array (123), nascent DNA replication strand sequencing (Repli-seq, 104), replication strand arrays (Repli-chip, 63), tandem mass spectrometry (MS/MS, 14), genotyping by high-throughput sequencing (genotyping HTS, 12) and DNA-PET (6) can be looked at in detail at https://www.encodeproject.org.
(Fig. 2a), 2,157,387 open chromatin regions, 750,392 regions with modified histones (mono-, di- or tri-methylation of histone H3 at lysine 4 (H3K4mel, H3K4me2 or H3K4me3), or acetylation of histone 3 at lysine 27 (H3K27ac)), 1,224,154 regions bound by transcription factors and chromatin-associated proteins (Fig. 2c), 845,000 RNA subregionsoccupied by RNA-binding proteins, and more than 130,000 long-range interactions between chromatin loci. These annotations have greatly enhanced our view of the human genomefrom its original annotation in 2003toa much richerand higher-resolution view(forexample,Fig. 2d,e). Indeed, although the number of human protein-coding genes known has changed only modestly, the number of transcript isoforms, long noncoding RNAs (IncRNAs), and potential regulatory regions identified has increased greatly since the project began (Fig. 2a-c). An important part of ENCODE 3 is that the regulatory mapping efforts have now been integrated and synthesized into the first version of an encyclopedia, highlighting a registry of 0.9 million cCREs in human and 0.3 million
cCREs in mouse. Details can be found in the accompanying ENCODE paper8 and companion papers in this issue and other journals914.
Technology, quality control and standards
Reaching the present annotation required a substantial expansion of technology development, from ENCODE groups and others, as well as the establishment of standards to ensure that the data are reproducible and of high quality. Most ENCODE 2 assays used sequence-based readouts (for example, RNA-seq1516 and ChlP-seq1718) rather than the array-based methods19,20 used in the pilot phase, and in ENCODE 3, methods such as global mappingof 3D interactions13 and RNA-binding regions14 were added. Throughout the project, computational and visualization approaches were developed for mapping reads and integrating different data types (Supplementary Note 1).
A key feature of ENCODE is theapplication of data standards, including the use of independent replicates (separate experiments on two or more biological samples521), except when precluded by the limited availability of materials (for example, postmortem human tissues). Of the 8,699 ENCODE 2and ENCODE 3 experiments, 6,101 have independent replicates. Of equal importance was the use of well-characterized reagents, such as antibodies for mapping sites of transcription factor binding, chromatin modifications and protein-RNA interactions22. ENCODE developed protocols to test each antibody 'lot' to demonstrate their experimental suitability, captured extensive metadata, and implemented controlled vocabularies and ontologies. Standards for reagents, experimental data, and metadata areon the ENCODE website: https://www.encodeproject.org/data-standards/.
Many metrics, including sequencing depth, mappingcharacteristics, replicate concordance, library complexity, and signal-to-noise ratio, were used to monitor the quality of each data set, and quality thresholds were applied21. A minority of experiments thatfell short of the standards (for example, insufficiently validated antibodies) are still reported, but are marked with a badge to indicate that an issue was found. This is a compromise for having some data versus none when an experiment did not meet ENCODE-defined thresholds.
An important component is uniform data processing. Data from the major ENCODE assays (ChlP-seq, DNase I hypersensitive sites sequencing (DNase-seq), RNA-seq, and whole-genome bisulfite sequencing (WGBS)) are uniformly processed and the processing pipelines are available for users to apply to their own data, by downloading the codefrom the GitHub (http://github.com/ENCODE-DCC) or by accessing the pipelines at the DNAnexus cloud provider. The standards and pipelines will continue to evolve as new technologies arise and are implemented.
The ENCODE Consortium isa good exampleof how large-scale group efforts can have a large impact on the scientific community, and many other national and international projects-including the NIH Road-map Epigenomics Program, The Cancer Genome Atlas (TCGA), the International Human Epigenome Consortium (IHEC), BLUEPRINT, the Canadian Epigenetics, Environment and Health Research Consortium (CEEHRC), the Genotype and Tissue Expression Project (GTEx), Psy-chENCODE, Functional Annotation of Animal Genomes (FAANG), the Global Alliance for Genomics and Health (GA4GH), the 4D Nucleome Program (4DN), the Human Cell Atlas and the FANTOM consortium-have nowformed (Supplementary Note 1). ENCODE has engaged with most of these consortia to share standards for data quality control, submission, and uniform processing and has helped to facilitate the use of common ontologies with some of these consortia. Data from the now-completed NIH Roadmap Epigenomics Program have been reprocessed and are available in the ENCODE database and are part of the Encyclopedia annotation. ENCODE continues to work with other consortia, individually and as part of the IHEC and GA4GH (for example, http://epishare-project.org) to increase data interoperability and the value of its resources.
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2012 ENCODE 2
2019
ENCODE 2, Roadmap & ENCODE 3
E 50 o
a 25
i 45.7% 1 36.9%	
20.6%                                                    ! 22.3% 11 o%M _ 13-0%	26.9% 1
	5.8% 1.1% 0.4% 0.3% 0.2%
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67,640,000 |
GENCODE version 7
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2019: GRCh38/hg38 chr16
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B cell cCREs
	IIB	llll	ii inn mi	llll			II
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				A	B cell H3K4me3		1
		i		^_. h._	B cell H3K27ac		-1
			i		B cell CTCF		i
Fig. 21 Progress in annotating the human genome. Link to high-resolution PDF file: https://www.dropbox.eom/s/rjdrcqygzl5p034/perspective. pdf ?dl=0. a, Improvement of gene annotations in the past 15 years by GENCODE, an international gene annotation group that uses ENCODE data42, b, ENCODE annotations in 2012 with phase II data. Bars show the percentages of the mappable human genome (3.1 billion nucleotides; hgl9) that were annotated as open chromatin by DNase-seq data, enriched in four types of active histone mark according to ChlP-seq data, and annotated as transcription factor binding sites (TFBSs) according to ChlP-seq data. Also shown are percentages of the genome assigned as transcription start sites (TSSs), enhancers and the insulator-binding protein (CTCF) by combining ChromHMM and Segway genome segmentations7, c, ENCODE annotations in 2019 with ENCODE2, Roadmap, and ENCODE 3data. The registry of cCREs developed during phase III defines 0.3%, 1.1%, 5.8%, 0.2% and 0.4% of the human
genome as cCREs with promoter-like signatures (PLS), proximal enhancer-like signatures (pELS), distal enhancer-like signatures (dELS), with high DNase, high H3K4me3 and low H3K27ac signals (DNase-H3K4me3), and bound by CTCF, respectively, d, A UCSC genome browser view of GENCODE genes (V7) coloured by transcript annotation (blue for coding, green for noncoding, and red for problematic) and combined genome segmentation (TSSs in red, enhancers in orange, weak enhancers in yellow, transcription in green, repressed in grey) at the C7~CFlocus on the hgl9 human genome, e, The UCSC genome browser view of GENCODE genes (V28, coloured as in d) and cCREs at the C7~CFlocus on the hg38 human genome8. Promoter-like, enhancer-like, and CTCF-only cCREs annotated in B cells are in red, yellow, and blue, respectively. The last four tracks show the DNase, H3K4me3, H3K27ac, and CTCF signals in B cells.
ENCODE as a resource
The purpose of ENCODE is to provide valuable, accessible resources to the community. ENCODE data and derived features are available from a publicly accessible data portal (https://www.encodeproject. org), and consent was obtained from donors to make data freely available to the public. Raw and processed data are available directly from the cloud as an Amazon Public Data Set (https://registry.opendata. aws/encode-project/). The data are widely used by the scientific community-more than 2,000 publications from researchers outside of ENCODE have used ENCODE data to study diverse topics (Fig. 3). Because most disease-associated common variants are noncoding and show substantial enrichment in candidate cell-type-specific els regulatory elements2124, ENCODE-derived resources, both in isolation and in conjunction with data from other resources (for example, GTEx), can help to identify and interpret disease-associated noncoding variants (Fig. 3a). Users engage with the data in many ways, ranging from
downloads of multiple data sets to detailed investigations of specific loci. Anyone navigating a major genome browser has access to thousands of biochemical, functional, and computational annotations to display at any genomic scale or to overlay on any sequence variant. Maps of epigenomic features relevant to gene regulation have been integrated to form a registry of discrete elements that are candidates for enhancers, promoters, or other regulatory elements. A specialized browser, SCREEN (http://screen.encodeproject.org), isan interface that can be used to identify and study these cCREs and associated ENCODE data and other annotations. This dynamic registry will be regularly updated as additional information is acquired.
Mouse ENCODE and modENCODE
Model organism studies have produced essential insights into almost every aspect of biology, including genomeorganization and function.
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2,400
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Community publications using ENCODE data ENCODE consortium publications
Year
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Human Autoimmunity, genetics,    allergy, 12% 12%
■ Cancer
■ Autoimmunity, allergy
■ Human genetics
■ Neurological/psychiatric
■ Cardiovascular
■ Multiple diseases
■ Metabolism Infectious disease
■ Musculoskeletal
■ Ageing
■ Ophthalmology
■ Developmental Respiratory
■ Haematology
■ Reproductive/dimorphism
■ Dermatological
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Fig. 31 Publications using ENCODE data. The National Human Genome Research Institute (NHGRI) has identified a list of publications that used ENCODE data. This list is publicly shared to provide examples illustrating how the resource has been used (https://www.encodeproject.org/publications/). a, Publications over time. Community publications appear to use ENCODE data and do not report ENCODE grant support in PubMed; consortium publications report ENCODE grant support in PubMed. In brief, communitypublications are identified using two steps; first, candidates are identified through automated searches for citation of ENCODE accession numbers, ENCODE flagship papers, or resources such as HaploReg and RegulomeDB; second, candidates are manually evaluated to determine whether ENCODE data were actually used. Consortium papers are identified through automated searches of PubMed for publications that were supported at least in part by ENCODE awards, and are not further evaluated or annotated, b, Human disease example publications. The subset of community publications that were annotated as 'human disease' (other categories are basic biology, software tool, fly/worm data) were further manually categorized by disease aetiology.
During ENCODE 2, mapping of mouse epigenomic and transcriptomic features was conducted in adult mouse tissues and cell lines through the Mouse ENCODE Project25, which identified 21,978 protein-coding regions, 32,168 noncoding genes, 1,192,301 open chromatin regions, 722,334 regions with modified histones H3K4mel, H3K4me2, H3K4me3, or H3K27ac, and 686,294 regions bound by transcription factors.
During ENCODE 2, a model organism ENCODE project (modENCODE26,27) was conducted to characterize the transcriptome, epige-nome, and transcription factor binding sites m Drosophila melanogaster and Caenorhabditiseleganstissues, developmental stagesand cell lines (Extended Data Fig. 1). These organisms provided the opportunity to develop detailed records of epigenomic features and transcriptome maps throughout development, which is difficult to accomplish in humans. Deep mapping of the spatial and temporal transcriptomes
of these species has substantially enhanced the annotation of both genomes. Similarly, detailed mapping of the regulatory circuits that govern gene regulation in Drosophila and C. elegans has provided insights into general principles of genome organization and function. Mapping of transcription factor binding sites in Drosophila and C. elegans has continued after modENCODE ended in a project called model organism Encyclopedia of Regulatory Networks (modERN) and to date has characterized more than 262 transcription factors in Drosophila and 217 transcription factors in C. elegans™. Collectively, the modENCODE Project has provided new insights about how the genomes of multicellular organisms direct development and maintain homeostasis.
In ENCODE phase III, experiments were carried out to characterize dynamic histone marks and accessibility, DNA methylomes, and transcriptomes in samples taken duringeight mouse fetal developmental stages with up to twelve tissues per stage28 30 (Fig. 4). The resulting more than 1,500 datasets comprise, to our knowledge, the most comprehensive study of epigenomes and transcriptomes during the prenatal development of a mammal. Integrative analysis of these datasets has expanded our knowledge of the transcriptional regulatory networks that regulate mammalian development and underscored the role of gene regulatory mechanisms in human disease. At least 214,264 of the candidate enhancers identified in fetal mouse tissues are conserved in the human genome8. The human orthologues of these potential regulatory elements are significantly enriched for genetic variants that are associated with common illnesses in a tissue-restricted manner, providing information for investigations of the molecular basis of human disease29,30.
The mouse data from ENCODE 3also include the results of more than 400 experiments using transgenic reporter mice designed to assess the function of cCREs in three embryonic tissues at two developmental stages. The results of this systematic study have helped to predict the in vivo activities of cCREs. For example, stronger enrichment for epigenetic signatures of enhancer activity correlated with higher rates of validation in the corresponding tissue2931.
Finally, comparisons of epigenome and transcriptome maps across species have led to insights into the evolution of transcribed regions and regulatory information25,32. Combinatorial histone modification patterns at as-regulatory elements and other genomic features are broadly conserved in metazoans. These chromatin states and transcript levels are highly correlated across tissues and developmental stages in all species examined. However, a notable fraction of specific ds-regulatory elements undergoes sequence and functional turnover during evolution, indicating that some regulatory components show substantial plasticity in their evolution whileoperating in a conserved regulatory network33.
Current limitations: phase IV and beyond
It is now apparent that elements that govern transcription, chromatin organization, splicing, and other key aspects of genome control and function are densely encoded in the human genome; however, despite the discovery of many new elements, the annotation of elements that are highly selective for particular cell types or states is lagging behind. For example, very few examples of condition-specific activation or repression of transcriptional control elementsare currently annotated in ENCODE. Similarly, information from human fetal tissue, reproductive organs and primary cell types is limited. In addition, although many open chromatin regions have been mapped, the transcription factors that bind to these sequences are largely unknown, and little attention has been devoted to the analysis of repetitive sequences. Finally, although transcript heterogeneity and isoforms have been described in many cell types, full-length transcripts that represent the isoform structure of spliced exons and edits have been described for only a small number of cell types.
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Fig. 41 An overview of the mouse ENCODE Project in the current phase.
a, Schematic representation of ENCODE 3 mouse developmental data series. The chromatin graphic is adapted from an image by Darryl Leja (NHGRI), Ian Dunham (EBI), and M.J.P. (NHGRI). The embryo image second from the right in was adapted from ref.+3, an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0. b, Three major axes of the data series: assays, tissues, and developmental stages. The region shown is chrll:98,307,637-98,344,383, mmlO.c, A schematic diagram of the transgenic assays used to validate and characterize the function of cCREs in Ell.5 and
High   Medium Modest ChlP-seq signal intensity
E12.5 mouse embryos. The cCREs were selected on the basis of ChlP-seq data and cloned into a reporter vector that was then introduced into fertilized mouse eggs. The activities oftheCRE were validated by tissue-specific expression patterns of the reporter gene, d, Results from recent transgenic assays8 29 to validate about 400 cCREs are summarized in a barchart, with the bars indicating the proportion of candidate CREs in each rank tier that showed reproducible reporter staining in the expected tissue (grey) or any tissue (pink).
Thus, as part of ENCODE 4, considerable effort is being devoted to expanding the cell types and tissues analysed (see URLs in Supplementary Note 1) as well as mapping the binding regions for many more transcription factors and RNA-binding proteins. These efforts are largely focused in a few reference cell lines, with the hope that improved knowledge will help with imputation or predictions in other cell states34. Single-cell transcriptome capture agents35 and open chromatin assays36 are also beingapplied to increase our understanding of the cellular heterogeneity of different tissues and samples. These efforts will supplement the many related activities that are also being pursued by HCA, HuBMAP and others37,38. Extensive mapping efforts of all types will continue in both the human and mouse, and parallel efforts to map transcription factor binding sites are being pursued in the Drosophlia and C. elegans by the modERN Project28. Full-length transcript isoforms are being elucidated in different cell types using long-read sequencing technologies39. ENCODE will continue to work with other consortia, and the data from different
groups and individual laboratories will need to be consolidated into a common repository.
Importantly, although very large numbers of noncoding elements have been defined, the functional annotation of ENCODE-identified elements is still in its infancy. High-throughput reporter-based assays40, CRISPR-based genome and epigenome editing methods41, and other high-throughput approaches are being used in the current phase of ENCODE to assess the functions of many thousands of elements and to relate those functional results to their biochemical signatures. These targeted functional assays, combined with the large-scale annotation of biochemical features, should further enhance the value of ENCODE data.
Through these and other efforts, it is expected that many more elements in the human genome will be identified across a variety of cell types and conditions, their activities will be revealed (often at the single-cell level), and their biological functions will be inferred more accurately. The development of a systems-wide understanding
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of function and integration with genetic information associated with human traits will greatly enhance our understanding of human biology and disease.
Online content
Any methods, additional references. Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availabilityareavailableat https://doi.org/10.1038/s41586-020-2449-8.
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3. LindbLad-Toh, K. et aL. A high-resoLution map of human evolutionary constraint using 29 mammals. Nature 478, 476-482 (2011).
4. Waterston, R. H. et aL. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520-562 (2002).
5. ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011).
6. Birney, E. etal. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816 (2007).
The results of the pilot phase of ENCODE included extensive functional assays across a selected one per cent of the human genome with experiments conducted on a variety of cell Lines and Largely with array-based technology.
7. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489,57-74 (2012).
The results of the second phase of ENCODE were based mostly on a Large number of genome-wide assays that Leveraged high-throughput sequencing technologies and were done across two 'tier one' cell Lines with Large-scale assays across several hund red cell and tissue types.
8. The ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature https://doi.org/10.1038/s41586-020-2493-4 (2020).
9. Partridge, E. C. et al. Occupancy maps of 208 chromatin-associated proteins in one human cell type. Nature https://doi.org/10.1038/s41586-020-2023-4 (2020).
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12. Breschi, A. et al. A Limited set of transcriptional programs define major cell types. Preprint at https://doi.org/10.1101/857169 (2020).
13. Grubert, F. et al. Landscape of cohesin-mediated chromatin Loops in the human genome. Nature https://doi.org/10.1038/s41586-020-2151-x (2020).
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Acknowledgements We thank S. Moore, E. CahiLL, M. KeLLis and J. Li for their assistance, and B. Wold for helpful comments. This work was supported by grants from the NIH: U01HG007019, U01HG007033, U01HG007036, U01HG007037, U41HG006992, U41HG006993, U41HG006994, U41HG006995, U41HG006996, U41HG006997, U41HG006998, U41HG006999, U41HG007000, U41HG007001, U41HG007002, U41HG007003, U41HG007234, U54HG006991, U54HG006997, U54HG006998, U54HG007004, U54HG007005, U54HG007010 and UM1HG009442.
Author contributions The role of the NHGRI Project Management Group in the preparation of this paper was Limited to coordination and scientific management of the ENCODE consortium. ALL other authors contributed to the concepts, writing and/or revisions of this manuscript.
Competing interests B.E.B. declares outside interests in Fulcrum Therapeutics, ICeLLBio, HiFiBio, Arsenal Biosciences, Cell Signaling Technologies, BioMillenia, and Nohla Therapeutics. P.F. is a member of the Scientific Advisory Boards of Fabric Genomics, Inc. and Eagle Genomics, Ltd. M.P.S. is cofounder and scientific advisory board member of Personalis, SensOmics, Mirvie, Qbio, January, Filtricine, and Genome Heart. He serves on the scientific advisory board of these companies and Genapsys and Jupiter. Z.W. is a cofounder of Rgenta Therapeutics and she serves on its scientific advisory board. R.M.M. is an advisor to DNAnexus and Decheng Capital, and has outside interests in IMIDomics, Accuragen and ReadCoor, Inc. The authors declare no other competing financial interests.
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Supplementary information is available for this paper at https://doi.org/10.1038/s41586-020-2449-8.
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698 I Nature | Vol 583 I 30 July 2020
The ENCODE Project Consortium
Federico AbascaL95, Reyes Acosta11, Nicholas J. Ad die man1, Jessika Adrian1, Veena AfzaL49, Bronwen Aken19, Jennifer A. Akiyama49, Omar AL JammaL15, Henry Amrhein43, Stacie M. Anderson44, Gregory R. Andrews4, Igor Antoshechkin43, Kristin G. Ardlie20, Joel Armstrong100, Matthew AstLey95, Budhaditya Banerjee87, Amira A. BarkaL86, If H. A. Barnes19, Iros Barozzi49, Daniel BarreLL19, Gemma Bar son95, Daniel Bates11, ULugbek K. Baymuradov1, Cassandra BaziLe35, Michael A. Beer98 ", Samantha Beik20, M. A. Bender76, Ruth Bennett19, Louis Philip Benoit Bouvrette37,38,39, Bradley E. Bernstein16, Andrew Berry19, Anand Bhaskar1, Alexandra Bignell19, Steven M. Blue36, David M. Bodine44, Carles Boix26, Nathan Boley1, Tyler Borrman4, Beatrice Borsari29, Alan P. Boyle58,59, Laurel A. Brandsmeier18, Alessandra Breschi29, Emery H. Bresnick93, Jason A. Brooks4, Michael Buckley11, Christopher B. Bürge35, Rachel Byron76, Eileen Cahill15, Ling ling Cai5, Lulu Cao1, Mark Carty96, Rosa G. Castanon48, And res Castillo11, Hassan Chaib1, Esther T. Chan1, Daniel R. Chee11, Sora Chee8, Hao Chen^6, Huaming Chen48, Jia-Yu Chen41, Songjie Chen1, J. Michael Cherry1, Surya B. ChhetrP7,18, Jyoti S. Choudhary107, Jacqueline Chrast101, Dongjun Chung92, Dec La n Clarke7, Neal A. L. Cody37,38,39, Candice J. Coppola17,18, Julie Coursen15, Anthony M. D'lppolito46, Stephen Dalton111, Cassidy Danyko3, Claire Davidson19, Jose Davila-Velderrain26, Carrie A. Davis3, Job Dekker77, ALden Deran100, Gilberto DeSalvo43, Gloria Despacio-Reyes95, Colin N. Dewey89, Diane E. Dickel49, Morgan Diegel11, Mark Diekhans100, Vishnu Dileep108, Bo Ding50, Sarah Djebali29,30, Alexander Dobin3, Daniel Dominguez35, Sarah Donaldson19, Jorg Drenkow32, Timothy R. Dreszer1, Yotam Drier21, Michael O. Duff4, Douglass Dunn11, Catharine Eastman60, Joseph R. Ecker48,57, Matthew D. Edwards26, Nicole El-Ali47, Shaimae I. Elhajjajy4, Keri Elkins36, Andrew Emili61, Charles B. Epstein20, Rachel C. Evans18, lakes Ezkurdia102, Kaili Fan4, Peggy J. Farnham63, Nina Farrell20, Elise A. Feingold15, Anne-Maud Ferreira101, Katherine Fisher-Ay lor43, Stephen Fitzgerald95, Paul Flicek19, Chuan Sheng Foo79, Kevin Fortier4, Adam Frankish19, Peter Freese40, Shaliu Fu5, Xiang-Dong Fu41, Yu Fu4,78, Yoko Fukuda-Yuzawa49, Mariateresa Fulciniti22, Alister P. W. Funnell11, Idan Gabdank1, Timur Galeev7, Mingshi Gao4, Carlos Garcia Giron19, Tyler H. Garvin49, Chelsea Anne Gelboin-Burkhart36, Grigorios Georgolopoulos11, Mark B. Gerstein7, Belinda M. Giardine10, David K. Gifford26, David M. Gilbert108, Daniel A. Gilchrist15, Shawn Gillespie21, Thomas R. Gingeras3, Peng Gong62, Alvaro Gonzalez96, Jose M. Gonzalez19, Peter Good117, Alon Goren20, David U. Gorkin8,9, Brenton R. Graveley14, Michael Gray95, Jack F. Greenblatt6171, Ed Griffiths95, Mark T. Groudine76, Fabian Grubert1, Mengting Gu7, Roderic Guigö29, Hongbo Guo61, Yu Guo63, Yuchun Guo26, Gamze Gursoy7, Maria Gutierrez-Arcelus87, Jessica Halow11, Ross C. Hardison10, Matthew Hardy19, Manoj Hariharan48, Arif Harmanci7, Anne Harrington49, Jennifer L. Harrow106, Tatsunori B. Hashimoto26, Richard D. Hasz112, Meital Hatan20, Eric Haugen11, James E. Hayes94, Peng He43, Yupeng He48, Nastaran Heidari1,64, David Hendrickson20, Elisabeth F. Heuston44, Jason A. Hilton1, Benjamin C. Hitz1, Abigail Hochman35, Cory Holgren65, Lei Hou26, Shuyu Hou5, Yun-Hua E. Hsiao97, Shanna Hsu20, Hui Huang8, Tim J. Hubbard105, Jack Huey4, Timothy R. Hughes61,73, Toby Hunt19, Sean Ibarrientos11, Robbyn Issner20, Mineo Iwata11, Osagie Izuogu19, Tommi Jaakkola26, Nader Jameel65, Camden Jansen47, Lixia Jiang1, Peng Jiang81,82, Audra Johnson11, Rory Johnson29,33, Irwin Jung reis20,26, Madhura Kadaba65, Maya Kasowski1, Mary Kasparian65, Momoe Kato49, Rajinder Kaul1113, Trupti Kawli1, Michael Kay19, Judith C. Keen113, Sunduz Keles88,89, Cheryl A. Keller10, David Kelley25, ManoLis Kellis20,26, Pouya Kheradpour26, Daniel Sunwook Kim1, Anthony Kirilusha43, Robert J. Klein94, Birgit Knoechel22,24, Samantha Kuan8, Michael J. Kulik110, Sushant Kumar7, Anshul Kundaje1, Tanya Kutyavin11, Julien Lagarde29, Bryan R. Lajoie77, Nicole J. Lambert35, John Lazar11, Ah Young Lee8, Donghoon Lee7, Elizabeth Lee49, Jin Wook Lee1, Kristen Lee11, Christina S. Leslie96, Shawn Levy18, Bin Li8, Hairi Li41, Nan Li50, Xiangrui Li5, Yang I. LP, Ying Li5, Yining LP, Yue Li26, Jin Lian62, Maxwell W. Libbrecht80, Shin Lin1, Yung Lin66, Dianbo Liu26, Jason Liu7, Peng Liu89, Tingting Liu53, X. Shirley Liu81,82, Yan Liu5, Yaping Liu26, Maria Long10, Shaoke Lou7, Jane Loveland19, Aiping Lu5, Yuheng Lu96, Eric Lecuyer37,38,39, Lijia Ma65, Mark Mackiewicz18, Brandon J. Mannion49, Michael Mannstadt21, Deepa Manthravadi95, Georgi K. Marinov43, Fergal J. Martin19, Eugenio Mattei4, Kenneth McCue43, Megan McEown18, Graham McVicker48, Sarah K. Meadows18, Alex Meissner27, Eric M. Menden ha ll1718, Christopher L. Messer18, Wouter Meuleman11, Clifford Meyer8182, Steve Miller95, Matthew G. Milton65, Tejaswini Mishra1, Dianna E. Moore18, Helen M. Moore114, Jill E. Moore4, Samuel H. Moore15, Jennifer Moran65, Ali Mortazavi47, Jonathan M. Mudge19, Nikhil Munshi22, Rabi Murad47, Richard M. Myers18, Vivek Nandakumar11, Preetha Nandi15, Anil M. Narasimha1, Aditi K. Narayanan1, Hannah Naughton15, Fabio C. P. Navarro7, Patrick Navas11, Jurijs Nazarovs88, Jemma Nelson11, Shane Neph11, Fidencio Jun Neri11, Joseph R. Nery48, Amy R. Nesmith18, J. Scott Newberry18, Kimberly M. Newberry18, Vu Ngo50, Rosy Nguyen18, Thai B. Nguyen36,Tung Nguyen50, Andrew Nishida11, William S. Noble12, Catherines. Novak49, Eva Maria Novoa26, Briana Nufiez15, Charles W. O'Donnell26, Sara Olson14, Kathrina C. 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Rhie63, John Rinn28, Miguel Rivera21, Juan Carlos Rivera-Mulia108109, Brian Roberts18, Jose Manuel Rodriguez103, Joel Rozowsky7, Russell Ryan21, Eric Rynes11, Denis N. Salins1, Richard Sandstrom11, Takayo Sasaki108, Shashank Sathe36, Daniel Savic42, Alexandra Scavelli3, Jonathan Scheiman23, Christoph Schlaff ner95, Jeffery A. Schloss15,
Frank W. Schmitges71, Lei Hoon See3, Anurag Sethi7, Manu Setty96, Anthony Shafer11, Shuo Shan4, Eilon Sharon1, Quan Shen62,72, Yin Shen8,51, Richard I. Sherwood87, Minyi Shi1, Sunyoung Shin90, Noam Shoresh20, Kyle Siebenthall11, Cristina Sisu7,104, Teri Slifer1, Cricket A. Sloan1, Anna Smith115, Valentina Snetkova49, Michael P. Snyder1,2, Damek V. Spacek1, Sharanya Srinivasan87, Rohith Srivas1, George Stamatoyannopoulos75, John A. Stamatoyannopoulos11,12,13, Rebecca Stanton36, Dave Steffan65, Sandra Steh ling-Sun11, J. Seth St rattan1, Amanda Su35, Balaji Sundararaman36, Marie-Marthe Suner19, Tahin Syed26, Matt Szynkarek65, Forrest Y. Tanaka1, Danielle Tenen20, Mingxiang Teng85, Jeffrey A. Thomas116, Dave Toffey65, Michael L. Tress103, Diane E. Trout43, Gosia Trynka95, Junko Tsuji4, Sean A. Upchurch43, Oana Ursu1, Barbara Uszczynska-Ratajczak29,34, Mia C. Uziel20, Alfonso Valencia103, Benjamin Van Biber11, Arjan G. van der Velde4,6, Eric L. Van Nostrand36, Yekaterina Vaydylevich15, Jesus Vazquez102, Alec Victorsen65, Jost Vielmetter43, Jeff Vierstra11, Axel Visel49,54,55, Anna Vlasova29, Christopher M. Vockley20, Simona Volpi15, Shinny Vong11, Hao Wang11, Mengchi Wang50, Qin Wang5, Ruth Wang36, Tao Wang50, Wei Wang50, Xiaofeng Wang37,38,39, Yanli Wang53, Nathaniel K. Watson1, Xintao Wei14, Zhijie Wei5, Hendrik Weisser95, Sherman M. Weissman62, Rene Welch89, Robert E. Welikson11, Zhiping Weng4,5,6, Harm-Jan Westra87, John W. Whitaker50, Collin White18, Kevin P. White74, Andre Wildberg50, Brian A. Williams43, David Wine20, Heather N. Witt63, Barbara Wold43, Maxim Wolf26, James Wright95, Rui Xiao41, Xinshu Xiao97, Jie Xu53, Jinrui Xu7, Koon-Kiu Yan7, Yongqi Yan11, Hong bo Yang53, Xinqiong Yang1, Yi-Wen Yang97, Galip Gürkan Yardimci12, Brian A. Yee36, Gene W. Yeo36, Taylor Young4, Tianxiong Yu5, Feng Yue52,53, Chris Zaleski3, Chongzhi Zang81,82,83, Haoyang Zeng26, Weihua Zeng47, Daniel R. Zerbino19, JieZhai1, Lijun Zhan14, Ye Zhan77, Bo Zhang53, Jialing Zhang62, Jing Zhang7, Kai Zhang50, Lijun Zhang53, Peng Zhang5, Qi Zhang91, Xiao-Ou Zhang4, Yanxiao Zhang8, Zhizhuo Zhang26, Yuan Zhao8, Ye Zheng88, Guoqing Zhong61, Xiao-Qiao Zhou15, Yun Zhu50 & Jared Zimmerman108
2CThe Broad Institute of Harvard and MIT, Cambridge, MA, USA. 2,MGH, Boston, MA, USA. ^Dana-Farber Cancer Institute, Boston, MA, USA. 23Harvard Medical School, Boston, MA, USA. 24Boston Children's Hospital, Boston, MA, USA. 25Harvard University, Cambridge, MA, USA. 26Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. 27Max Planck Institute for Molecular Genetics, Department of Genome Regulation, Berlin, Germany. 23University of Colorado Boulder, Boulder, CO, USA. 29Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology and Universität Pompeu Fabra, Barcelona, Spain. 3CIRSD, Universite de Toulouse, INSERM, IN RA, EN VT, UPS, U1220, CHU Purpan, CS60039, Toulouse, France. 3,Skolkovo Institute for Science and Technology, Moscow, Russia. 32FunctionaLGenomics, Cold Spring Harbor Laboratory, Woodbury, NY, USA. 33Department of Clinical Research, University of Bern, Bern, Switzerland. ^International Institute of Molecular and Cell Biology, Warsaw, Poland. 35Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. 36Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, Stem Cell Program, Sanford Consortium for Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA. 37Departement de Biochimie et Medecine Moleculaire, Universite de Montreal, Montreal, Quebec, Canada. 33Division of Experimental Medicine, McGiLL University, Quebec, Canada. 39lnstitut de Recherches CLiniques de Montreal(IRCM), Montreal, Quebec, Canada. 4CProgram in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. 4,Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, San Diego, CA, USA. 42Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA. 43Division of Biology, California Institute of Technology, Pasadena, CA, USA. 44National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. 45Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. 46Center for Genomic and Computational Biology, Duke University, Durham, NC, USA. 47Biological Sciences, University of California, Irvine, Irvine, CA, USA. 43Salk Institute for Biological Studies, La Jolla, CA, USA. ^Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. 5CDepartment of Chemistry and Biochemistry, Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, USA. ^Institute for Human Genetics, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA. ^Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 53Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA. 54US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. 55School of Natural Sciences, University of California, Merced, Merced, CA, USA. 56Comparative Biochemistry Program, University of California, Berkeley, CA, USA. 57Howard Hughes Medical Institute, TheSalk Institute for Biological Studies, La Jolla, CA, USA. 53Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml, USA. 59Department of Human Genetics, University of Michigan, Ann Arbor, Ml, USA. 6CDepartment of Molecular and Cellular Physiology, School of Medicine, Stanford University, Palo Alto, CA, USA. 6,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. 62Department of Genetics, School of Medicine, Yale University, New Haven, CT, USA. 63Department of Biochemistry and Molecular Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. 64Department of Radiation Oncology, School of Medicine, Stanford University, Palo Alto, CA, USA. 65Department of Human Genetics, Institute for Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA. 66Division of General Surgery, Section of Transplant Surgery, School of Medicine, Washington University, St. Louis, MO, USA. 67Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical
Perspective
University, Guangzhou, China. 63Guangdong Provincial Key Laboratory of Single CeU Technology and Application, Guangzhou, China. "Department of Cell Biology & Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 7CThurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 7,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. 72School of Medicine, Jiangsu University, Zhenjiang, China. 73Department of Molecular Genetics, Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. 74Tempus Labs, Chicago, IL, USA. 75Department of Medicine, University of Washington, Seattle, WA, USA. 76Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 77HHMI and Program in Systems Biology, University of Massachusetts Medical School, Albert Sherman Center, Worcester, MA, USA. 73University of Massachusetts Amherst, Amherst, MA, USA. "Institute for Infocomm Research, Singapore, Singapore. 3CSimon Fraser University, Burnaby, British Columbia, Canada. ^'Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. 32Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA. 33Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. 34Molecular Pathology Unit & Cancer Center, Boston, MA, USA. 35Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA. ^Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. ^Department of Medicine, Brig ham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ^Department of Statistics, Medical Sciences Center, Madison, Wl, USA. "Department of Biostatistics and Medical Informatics, Madison, Wl, USA. 9CDepartment of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA. 9'Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA. 92Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. "Department of Cell and
Regenerative Biology, UW-Madison Blood Research Program, Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin, Madison, Wl, USA. 94Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. "Wellcome Sanger Institute, Cambridge, UK. 96Program in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 97610 Charles E.Young Drive S, Terasaki Life Sciences Building, Room2000E, Los Angeles, CA, USA. "McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. '""University of California, Santa Cruz, Santa Cruz, CA, USA. '"'Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. ,C2Centro Nacionalde Investigaciones Cardiovasculares (CNIC) and CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. '"Spanish National Cancer Research Centre (CNIO), Madrid, Spain. 'C4Brunel University London, London, UK. ,C5King's College London, Guy's Hospital, London, UK. ,C6ELIXIR Hub, Wellcome Genome Campus, Cambridge, UK. ,07lnstitute of Cancer Research, London, UK. '"Department of Biological Science, Florida State University, Tallahassee, FL, USA. '"Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, Minneapolis, MN, USA. ""Center for Vaccines and Immunology University of Georgia, Athens, GA, USA. '"Center for Molecular Medicine and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA. "2Gift of Life Donor Program, Philadelphia, PA, USA. "3American Society for Radiation Oncology, Arlington, VA, USA. '"National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. "5Leidos Biomedical, Inc, Frederick, MD, USA. "6National Disease Research Interchange (NDRI), Philadelphia, PA, USA. "74407 Puller Drive, Kensington, MD, USA.
ENCODE Timeline
2003 - 2007 2008 - 2012 2013 - 2016 2017 - 2021
Pilot Phase 1	Phase 2				Phase 3		Phase 4
Pilot Phase (1 %) Data Production (Human)	Data Production (DCC & DAC) (Human)				Data Production (DCC & DAC) (Human & Mouse)		Data Production (DCC & DAC) (Human & Mouse)
							
					Computational Analysis 1		Computational Analysis 2
							
modENCODE (Fly & Worm)							Functional Characterization
							
		Mouse ENCODE					
							
Technology Development 1 & 2	Technology Development 3				Technology Development 4		
							
Extended Data Fig. 11 ENCODE timeline. Pilot phase: September 2003-September 2007; ENCODE 2: September 2007-September 2012; ENCODE 3: September 2012-January 2017; ENCODE4: February 2017-present; modENCODE: April 2007-April 2012;mouse ENCODE: 2009-2012.