The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.
Interpreting the human genome sequence is one of the leading challenges of 21st century biology [1]. In 2003, the National Human Genome Research Institute (NHGRI) embarked on an ambitious project—the Encyclopedia of DNA Elements (ENCODE) —aiming to delineate all of the functional elements encoded in the human genome sequence [2]. To further this goal, NHGRI organized the ENCODE Consortium, an international group of investigators with diverse backgrounds and expertise in production and analysis of high-throughput functional genomic data. In a pilot project phase spanning 2003–2007, the Consortium applied and compared a variety of experimental and computational methods to annotate functional elements in a defined 1% of the human genome [3]. Two additional goals of the pilot ENCODE Project were to develop and advance technologies for annotating the human genome, with the combined aims of achieving higher accuracy, completeness, and cost-effective throughput and establishing a paradigm for sharing functional genomics data. In 2007, the ENCODE Project was expanded to study the entire human genome, capitalizing on experimental and computational technology developments during the pilot project period. Here we describe this expanded project, which we refer to throughout as the ENCODE Project, or ENCODE. The major goal of ENCODE is to provide the scientific community with high-quality, comprehensive annotations of candidate functional elements in the human genome. For the purposes of this article, the term “functional element” is used to denote a discrete region of the genome that encodes a defined product (e. g. , protein) or a reproducible biochemical signature, such as transcription or a specific chromatin structure. It is now widely appreciated that such signatures, either alone or in combinations, mark genomic sequences with important functions, including exons, sites of RNA processing, and transcriptional regulatory elements such as promoters, enhancers, silencers, and insulators. However, it is also important to recognize that while certain biochemical signatures may be associated with specific functions, our present state of knowledge may not yet permit definitive declaration of the ultimate biological role (s), function (s), or mechanism (s) of action of any given genomic element. At present, the proportion of the human genome that encodes functional elements is unknown. Estimates based on comparative genomic analyses suggest that 3%–8% of the base pairs in the human genome are under purifying (or negative) selection [4]–[7]. However, this likely underestimates the prevalence of functional features, as current comparative methods may not account for lineage-specific evolutionary innovations, functional elements that are very small or fragmented [8], elements that are rapidly evolving or subject to nearly neutral evolutionary processes, or elements that lie in repetitive regions of the genome. The current phase of the ENCODE Project has focused on completing two major classes of annotations: genes (both protein-coding and non-coding) and their RNA transcripts, and transcriptional regulatory regions. To accomplish these goals, seven ENCODE Data Production Centers encompassing 27 institutions have been organized to focus on generating multiple complementary types of genome-wide data (Figure 1 and Figure S1). These data include identification and quantification of RNA species in whole cells and in sub-cellular compartments, mapping of protein-coding regions, delineation of chromatin and DNA accessibility and structure with nucleases and chemical probes, mapping of histone modifications and transcription factor (TF) binding sites by chromatin immunoprecipitation (ChIP), and measurement of DNA methylation (Figure 2 and Table 1). In parallel with the major production efforts, several smaller-scale efforts are examining long-range chromatin interactions, localizing binding proteins on RNA, identifying transcriptional silencer elements, and understanding detailed promoter sequence architecture in a subset of the genome (Figure 1 and Table 1). ENCODE has placed emphasis on data quality, including ongoing development and application of standards for data reproducibility and the collection of associated experimental information (i. e. , metadata). Adoption of state-of-the-art, massively parallel DNA sequence analysis technologies has greatly facilitated standardized data processing, comparison, and integration [9], [10]. Primary and processed data, as well as relevant experimental methods and parameters, are collected by a central Data Coordination Center (DCC) for curation, quality review, visualization, and dissemination (Figure 1). The Consortium releases data rapidly to the public through a web-accessible database (http: //genome. ucsc. edu/ENCODE/) [11] and provides a visualization framework and analytical tools to facilitate use of the data [12], which are organized into a web portal (http: //encodeproject. org). To facilitate comparison and integration of data, ENCODE data production efforts have prioritized selected sets of cell types (Table 2). The highest priority set (designated “Tier 1”) includes two widely studied immortalized cell lines—K562 erythroleukemia cells [13]; an EBV-immortalized B-lymphoblastoid line (GM12878, also being studied by the 1,000 Genomes Project; http: //1000genomes. org) and the H1 human embryonic stem cell line [14]. A secondary priority set (Tier 2) includes HeLa-S3 cervical carcinoma cells [15], HepG2 hepatoblastoma cells [16], and primary (non-transformed) human umbilical vein endothelial cells (HUVEC; [17]), which have limited proliferation potential in culture. To capture a broader spectrum of human biological diversity, a third set (Tier 3) currently comprises more than 100 cell types that are being analyzed in selected assays (Table 2). Standardized growth conditions for all ENCODE cell types have been established and are available through the ENCODE web portal (http: //encodeproject. org, “cell types” link). This report is intended to provide a guide to the data and resources generated by the ENCODE Project to date on Tier 1–3 cell types. We summarize the current state of ENCODE by describing the experimental and computational approaches used to generate and analyze data. In addition, we outline how to access datasets and provide examples of their use.
Cis-regulatory regions include diverse functional elements (e. g. , promoters, enhancers, silencers, and insulators) that collectively modulate the magnitude, timing, and cell-specificity of gene expression [35]. The ENCODE Project is using multiple approaches to identify cis-regulatory regions, including localizing their characteristic chromatin signatures and identifying sites of occupancy of sequence-specific transcription factors. These approaches are being combined to create a comprehensive map of human cis-regulatory regions. ENCODE is also generating additional data types to complement production projects and benchmark novel technologies. An overview of these datasets is provided in Table 1. With the aim of ensuring quality and consistency, ENCODE has defined standards for collecting and processing each data type. These standards encompass all major experimental components, including cell growth conditions, antibody characterization, requirements for controls and biological replicates, and assessment of reproducibility. Standard formats for data submission are used that capture all relevant data parameters and experimental conditions, and these are available at the public ENCODE portal (http: //genome. ucsc. edu/ENCODE/dataStandards. html). All ENCODE data are reviewed by a dedicated quality assurance team at the Data Coordination Center before release to the public. Experiments are considered to be verified when two highly concordant biological replicates have been obtained with the same experimental technique. In addition, a key quality goal of ENCODE is to provide validation at multiple levels, which can be further buttressed by cross-correlation between disparate data types. For example, we routinely perform parallel analysis of the same biological samples with alternate detection technologies (for example, ChIP-seq versus ChIP-chip or ChIP-qPCR). We have also compared our genome-wide results to “gold-standard” data from individual locus studies, such as DNase-seq versus independently performed conventional (Southern-based) DNaseI hypersensitivity studies. Cross-correlation of independent but related ENCODE data types with one another, such as DNaseI hypersensitivity, FAIRE, transcription factor occupancy, and histone modification patterns, can provide added confidence in the identification of specific DNA elements. Similarly, cross-correlation between long RNA-seq, CAGE, and TAF1 ChIP-seq data can strengthen confidence in a candidate location for transcription initiation. Finally, ENCODE is performing pilot tests for the biological activity of DNA elements to the predictive potential of various ENCODE biochemical signatures for certain biological functions. Examples include transfection assays in cultured human cells and injection assays in fish embryos to test for enhancer, silencer, or insulator activities in DNA elements identified by binding of specific groups of TFs or the presence of DNaseI hypersensitive sites or certain chromatin marks. Ultimately, defining the full biological role of a DNA element in its native chromosomal location and organismic context is the greatest challenge. ENCODE is beginning to approach this by integrating its data with results from other studies of in situ knockouts and/or knockdowns, or the identification of specific naturally occurring single base mutations and small deletions associated with changes in gene expression. However, we expect that deep insights into the function of most elements will ultimately come from the community of biologists who will build on ENCODE data or use them to complement their own experiments. A catalog of ENCODE datasets is available at http: //encodeproject. org. These data provide evidence that ∼1 Gigabase (Gb; 32%) of the human genome sequence is represented in steady-state, predominantly processed RNA populations. We have also delineated more than 2 million potential regulatory DNA regions through chromatin and TF mapping studies. The assessment of the completeness of detection of any given element is challenging. To analyze the detection of transcripts in a single experiment, we have sequenced to substantial depth and used a sampling approach to estimate the number of reads needed to approach complete sampling of the RNA population (Figure 6A) [104]. For example, analyzing RNA transcripts with about 80 million mapped reads yields robust quantification of more than 80% of the lowest abundance class of genes (2–19 reads per kilobase per million mapped tags, RPKM) [24]. Measuring RNAs across multiple cell types, we find that, after the analysis of seven cell lines, 68% of the GENCODE transcripts can be detected with RPKM >1. In the case of regulatory DNA, we have analyzed the detection of regulatory DNA by using three approaches: 1) the saturation of occupancy site discovery for a single transcription factor within a single cell type as a function of sequencing read depth, 2) the incremental discovery of DNaseI hypersensitive sites or the occupancy sites for a single TF across multiple cell types, and 3) the incremental rate of collective TF occupancy site discovery for all TFs across multiple cell types. For detecting TF binding sites by ChIP-seq, we have found that the number of significant binding sites increases as a function of sequencing depth and that this number varies widely by transcription factor. For example, as shown in Figure 6B, 90% of detectable sites for the transcription factor GABP can be identified by using the MACS peak calling program at a depth of 24 million reads, whereas only 55% of detectable RNA Pol2 sites are identified at this depth when an antibody that recognizes both initiating and elongating forms of the enzyme is used. Even at 50 million reads, the number of sites is not saturated for RNA Pol2 with this antibody. It is important to note that determinations of saturation may vary with the use of different antibodies and laboratory protocols. For instance, a different RNA Pol2 antibody that recognizes unphosphorylated, non-elongating RNA Pol2 bound only at promoters requires fewer reads to reach saturation [105]. For practical purposes, ENCODE currently uses a minimum sequencing depth of 20 M uniquely mapped reads for sequence-specific transcription factors. For data generated prior to June 1,2010, this figure was 12 M. To assess the incremental discovery of regulatory DNA across different cell types, it was necessary to account for the non-uniform correlation between cell lines and assays (see Figure 6C legend for details). We therefore examined all possible orderings of either cell types or assays and calculated the distribution of elements discovered as the number of cell types or assays increases, presented as saturation distribution plots (Figure 6C and 6D, respectively). For DNase hypersensitive sites, we observe a steady increase in the mean number of sites discovered as additional cell types are tested up to and including the 62 different cell types examined to date, indicating that new elements continue to be identified at a relatively high rate as additional cell types are sampled (Figure 6C). Analysis of CTCF sites across 28 cell types using this approach shows similar behavior. Analysis of binding sites for 42 TFs in the cell line with most data (K562) also shows that saturation of the binding sites for these factors has not yet been achieved. These results indicate that additional cell lines need to be analyzed for DNaseI and many transcription factors, and that many more transcription factors need to be analyzed within single cell types to capture all the regulatory information for a given factor across the genome. The implications of these trends for defining the extent of regulatory DNA within the human genome sequence is as yet unclear.
The ENCODE Data Release and Use Policy is described at http: //www. encodeproject. org/ENCODE/terms. html. Briefly, ENCODE data are released for viewing in a publicly accessible browser (initially at http: //genome-preview. ucsc. edu/ENCODE and, after additional quality checks, at http: //encodeproject. org). The data are available for download and pre-publication analysis of any kind, as soon as they are verified (i. e. , shown to be reproducible). However, consistent with the principles stated in the Toronto Genomic Data Use Agreement [106], the ENCODE Consortium data producers request that they have the first publication on genome-wide analyses of ENCODE data, within a 9-month timeline from its submission. The timeline for each dataset is clearly displayed in the information section for each dataset. This parallels policies of other large consortia, such as the HapMap Project (http: //www. hapmap. org), that attempt to balance the goal of rapid data release with the ability of data producers to publish initial analyses of their work. Once a producer has published a dataset during this 9-month period, anyone may publish freely on the data. The embargo applies only to global analysis, and the ENCODE Consortium expects and encourages immediate use and publication of information at one or a few loci, without any consultation or permission. For such uses, identifying ENCODE as the source of the data by citing this article is requested. After curation and review at the Data Coordination Center, all processed ENCODE data are publicly released to the UCSC Genome Browser database (http: //genome. ucsc. edu). Accessioning of ENCODE data at the NCBI Gene Expression Omnibus (GEO; http: //www. ncbi. nlm. nih. gov/geo/info/ENCODE. html) is underway. Primary DNA sequence reads are stored at UCSC and the NCBI Sequence Read Archive (SRA; http: //www. ncbi. nlm. nih. gov/Traces/sra/sra. cgi?) and will also be retrievable via GEO. Primary data derived from DNA microarrays (for example, for gene expression) are deposited directly to GEO. The processed data are also formatted for viewing in the UCSC browser. Metadata, including information on antibodies, cell culture conditions, and other experimental parameters, are deposited into the UCSC database, as are results of validation experiments. Easy retrieval of ENCODE data to a user' s desktop is facilitated by the UCSC Table Browser tool (http: //genome. ucsc. edu/cgi-bin/hgTables? org=human), which does not require programming skills. Computationally sophisticated users may gain direct access to data through application programming interfaces (APIs) at both the UCSC browser and NCBI and by downloading files from http: //genome. ucsc. edu/ENCODE/downloads. html. An overview of ENCODE data types and the location of the data repository for each type is presented in Table 4.
Many users will want to view and interpret the ENCODE data for particular genes of interest. At the online ENCODE portal (http: //encodeproject. org), users should follow a “Genome Browser” link to visualize the data in the context of other genome annotations. Currently, it is useful for users to examine both the hg18 and the hg19 genome browsers. The hg18 has the ENCODE Integrated Regulation Track on by default, which shows a huge amount of data in a small amount of space. The hg19 browser has newer datasets, and more ENCODE data than are available on hg18. Work is in progress to remap the older hg18 datasets to hg19 and generate integrated ENCODE tracks. On either browser, additional ENCODE tracks are marked by a double helix logo in the browser track groups for genes, transcripts, and regulatory features. Users can turn tracks on or off to develop the views most useful to them (Figure 7). To aid users in navigating the rich variety of data tracks, the ENCODE portal also provides a detailed online tutorial that covers data display, data download, and analysis functions available through the browser. Examples applying ENCODE data at individual loci to specific biological or medical issues are a good starting point for exploration and use of the data. Thus, we also provide a collection of examples at the “session gallery” at the ENCODE portal. Users are encouraged to submit additional examples; we anticipate that this community-based sharing of insights will accelerate the use and impact of the ENCODE data. Numerous genome-wide association studies (GWAS) that link human genome sequence variants with the risk of disease or with common quantitative phenotypes have now become available. However, in most cases, the molecular consequences of disease- or trait-associated variants for human physiology are not understood [107]. In more than 400 studies compiled in the GWAS catalog [108], only a small minority of the trait/disease-associated SNPs (TASs) occur in protein-coding regions; the large majority (89%) are in noncoding regions. We therefore expect that the accumulating functional annotation of the genome by ENCODE will contribute substantially to functional interpretation of these TASs. For example, common variants within a ∼1 Mb region upstream of the c-Myc proto-oncogene at 8q24 have been associated with cancers of the colon, prostate, and breast (Figure 8A) [109]–[111]. ENCODE data on transcripts, histone modifications, DNase hypersensitive sites, and TF occupancy show strong, localized signals in the vicinity of major cancer-associated SNPs. One variant (rs698327) lies within a DNase hypersensitive site that is bound by several TFs and the enhancer-associated protein p300 and contains histone modification patterns typical of enhancers (high H3K4me1, low H3K4me3; Figure 8B). Recent studies have shown enhancer activity and allele-specific binding of TCF7L2 at this site [112], with the risk allele showing greater binding and activity [113], [114]. Moreover, this element appears to contact the downstream c-Myc gene in vivo, compatible with enhancer function [114], [115]. Similarly, several regions predicted via ENCODE data to be involved in gene regulation are close to SNPs in the BCL11A gene associated with persistent expression of fetal hemoglobin (Figure S2). These examples show that the simple overlay of ENCODE data with candidate non-coding risk-associated variants may readily identify specific genomic elements as leading candidates for investigation as probable effectors of phenotypic effects via alterations in gene expression or other genomic regulatory processes. Importantly, even data from cell types not directly associated with the phenotype of interest may be of considerable value for hypothesis generation. It is reasonable to expect that application of current and future ENCODE data will provide useful information concerning the mechanism (s) whereby genomic variation influences susceptibility to disease, which then can then be tested experimentally. All ENCODE datasets to date are from populations of cells. Therefore, the resulting data integrate over the entire cell population, which may be physiologically and genetically inhomogeneous. Thus, the source cell cultures in the ENCODE experiments are not typically synchronized with respect to the cell cycle and, as with all such samples, local micro-environments in culture may also vary, leading to physiological differences in cell state within each culture. In addition, one Tier 1 cell line (K562) and two Tier 2 cell lines (HepG2 and HeLa) are known to have abnormal genomes and karyotypes, with genome instability. Finally, some future Tier 3 tissue samples or primary cultures may be inherently heterogeneous in cell type composition. Averaging over heterogeneity in physiology and/or genotype produces an amalgamation of the contributing patterns of gene expression, factor occupancy, and chromatin status that must be considered when using the data. Future improvements in genome-wide methodology that allow the use of much smaller amounts of primary samples, or follow-up experiments in single cells when possible, may allow us to overcome many of these caveats. The use of DNA sequencing to annotate functional genomic features is constrained by the ability to place short sequence reads accurately within the human genome sequence. Most ENCODE data types currently represented in the UCSC browser use only those sequence reads that map uniquely to the genome. Thus, centromeric and telomeric segments (collectively ∼15% of the genome and enriched in recent transposon insertions and segmental duplications) as well as sequences not present in the current genome sequence build [116] are not subject to reliable annotation by our current techniques. However, such information can be gleaned through mining of the publicly available raw sequence read datasets generated by ENCODE. It is useful to recognize that the confidence with which different classes of ENCODE elements can be related to a candidate function varies. For example, ENCODE can identify with high confidence new internal exons of protein-coding genes, based on RNA-seq data for long polyA+ RNA. Other features, such as candidate promoters, can be identified with less, yet still good, confidence by combining data from RNA-seq, CAGE-tags, and RNA polymerase 2 (RNA Pol2) and TAF1 occupancy. Still other ENCODE biochemical signatures come with much lower confidence about function, such as a candidate transcriptional enhancer supported by ChIP-seq evidence for binding of a single transcription factor. Identification of genomic regions enriched by ENCODE biochemical assays relies on the application of statistical analyses and the selection of threshold significance levels, which may vary between the algorithms used for particular data types. Accordingly, discrete annotations, such as TF occupancy or DNaseI hypersensitive sites, should be considered in the context of reported p values, q values, or false discovery rates, which are conservative in many cases. For data types that lack focal enrichment, such as certain histone modifications and many RNA Pol2-bound regions, broad segments of significant enrichment have been delineated that encompass considerable quantitative variation in the signal strength along the genome.
Development and implementation of algorithms and pipelines for processing and analyzing data has been a major activity of the ENCODE Project. Because massively parallel DNA sequencing has been the main type of data generated by the Consortium, much of the algorithmic development and data analysis to date has been concerned with issues related to producing and interpreting such data. Software packages and algorithms commonly used in the ENCODE Consortium are summarized in Tables 3 and S1. In general, the analysis of sequencing-based measurements of functional or biochemical genomic parameters proceeds through three major phases. In the first phase, the short sequences that are the output of the experimental method are aligned to the reference genome. Algorithm development for efficient and accurate alignment of short read sequences to the human genome is a rapidly developing field, and ENCODE groups employ a variety of the state-of-the-art software (see Tables 3 and S1). In the second phase, the initial sequence mapping is processed to identify significantly enriched regions from the read density. For ChIP-seq (TFs and histone modification), DNase-seq or FAIRE-seq, both highly localized peaks or broader enriched regions may be identified. Within the ENCODE Consortium, each data production group provides lists of enriched regions or elements within their own data, which are available through the ENCODE portal. It should be noted that, for most data types, the majority of enriched regions show relatively weak absolute signal, necessitating the application of conservative statistical thresholds. For some data, such as those derived from sampling RNA species (e. g. , RNA-seq), additional algorithms and processing are used to handle transcript structures and the recognition of splicing events. The final stage of analysis involves integrating the identified regions of enriched signal with each other and with other data types. An important prerequisite to data integration is the availability of uniformly processed datasets. Therefore, in addition to the processing pipelines developed by individual production groups, ENCODE has devoted considerable effort toward establishing robust uniform processing for phases 1 and 2 to enable integration. For signal comparison, specific consideration has been given to deriving a normalized view of the sequence read density of each experiment. In the case of ChIP-seq for TFs, this process includes in silico extension of the sequence alignment to reflect the experimentally determined average lengths of the input DNA molecules that are sampled by the short sequence tag, compensation for repetitive sequences that may lead to alignment with multiple genomic locations, and consideration of the read density of the relevant control or input chromatin experiment. ENCODE has adopted a uniform standardized peak-calling approach for transcription factor ChIP-seq, including a robust and conservative replicate reconciliation statistic (Irreproducible Discovery Rate, IDR [117], to yield comparable consensus peak calls. As the project continues, we expect further standardizations to be developed. There are many different ways to analyze and integrate large, diverse datasets. Some of the basic approaches include assigning features to existing annotations (e. g. , assigning transcribed regions to annotated genes or Pol2-binding peaks to likely genes), discovery of correlations among features, and identification of particular gene classes (e. g. , Gene Ontology categories) preferentially highlighted by a given annotation. Many software tools exist in the community for these purposes, including some developed within the ENCODE Project, such as the Genome Structure Correction statistic for assessing overlap significance [3]. Software tools used for integration by ENCODE are summarized in Tables 3 and S1.
The challenge of achieving complete coverage of all functional elements in the human genome is substantial. The adult human body contains several hundred distinct cell types, each of which expresses a unique subset of the ∼1,500 TFs encoded in the human genome [118]. Furthermore, the brain alone contains thousands of types of neurons that are likely to express not only different sets of TFs but also a larger variety of non-coding RNAs [119]. In addition, each cell type may exhibit a diverse array of responses to exogenous stimuli such as environmental conditions or chemical agents. Broad areas of fundamental chromosome function, such as meiosis and recombination, remain unexplored. Furthermore, ENCODE has focused chiefly on definitive cells and cell lines, bypassing the substantial complexity of development and differentiation. A truly comprehensive atlas of human functional elements is not practical with current technologies, motivating our focus on performing the available assays in a range of cell types that will provide substantial near-term utility. ENCODE is currently developing a strategy for addressing this cellular space in a timely manner that maximizes the value to the scientific community. Feedback from the user community will be a critical component of this process. To understand better and functionally annotate the human genome, ENCODE is making efforts to analyze and integrate data within the project and with other large-scale projects. These efforts include 1) defining promoter and enhancer regions by combining transcript mapping and biochemical marks, 2) delineating distinct classes of regions within the genomic landscape by their specific combinations of biochemical and functional characteristics, and 3) defining transcription factor co-associations and regulatory networks. These efforts aim to extend our understanding of the functions of the different biochemical elements in gene regulation and gene expression. One of the major motivations for the ENCODE Project has been to aid in the interpretation of human genome variation that is associated with disease or quantitative phenotypes. The Consortium is therefore working to combine ENCODE data with those from other large-scale studies, including the 1,000 Genomes Project, to study, for example, how SNPs and structural variation may affect transcript, regulatory, and DNA methylation data. We foresee a time in the near future when the biochemical features defined by ENCODE are routinely combined with GWAS and other sequence variation–driven studies of human phenotypes. Analogously, the systematic profiling of epigenomic features across ex vivo tissues and stem cells currently being undertaken by the NIH Roadmap Epigenomics program will provide synergistic data and the opportunity to observe the state and behavior of ENCODE-identified elements in human tissues representing healthy and disease states. These are but a few of many applications of the ENCODE data. Investigators focused on one or a few genes should find many new insights within the ENCODE data. Indeed, these investigators are in the best position to infer potential functions and mechanisms from the ENCODE data—ones that will also lead to testable hypotheses. Thus, we expect that the work of many investigators will be enhanced by these data and that their results will in turn inform the development of the project going forward. Finally, we also expect that comprehensive paradigms for gene regulation will begin to emerge from our work and similar work from many laboratories. Deciphering the “regulatory code” within the genome and its associated epigenetic signals is a grand and complex challenge. The data contributed by ENCODE in conjunction with complementary efforts will be foundational to this effort, but equally important will be novel methods for genome-wide analysis, model building, and hypothesis testing. We therefore expect the ENCODE Project to be a major contributor not only of data but also novel technologies for deciphering the human genome and those of other organisms.
The Encyclopedia of DNA Elements (ENCODE) Project was created to enable the scientific and medical communities to interpret the human genome sequence and to use it to understand human biology and improve health. The ENCODE Consortium, a large group of scientists from around the world, uses a variety of experimental methods to identify and describe the regions of the 3 billion base-pair human genome that are important for function. Using experimental, computational, and statistical analyses, we aimed to discover and describe genes, transcripts, and transcriptional regulatory regions, as well as DNA binding proteins that interact with regulatory regions in the genome, including transcription factors, different versions of histones and other markers, and DNA methylation patterns that define states of the genome in various cell types. The ENCODE Project has developed standards for each experiment type to ensure high-quality, reproducible data and novel algorithms to facilitate analysis. All data and derived results are made available through a freely accessible database. This article provides an overview of the complete project and the resources it is generating, as well as examples to illustrate the application of ENCODE data as a user' s guide to facilitate the interpretation of the human genome.
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A User's Guide to the Encyclopedia of DNA Elements (ENCODE)