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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nature. Author manuscript; available in PMC Mar 6, 2013.
Published in final edited form as:
PMCID: PMC3439153

An Integrated Encyclopedia of DNA Elements in the Human Genome

The ENCODE Project Consortium


The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.

The human genome sequence provides the underlying code for human biology. Despite intensive study, especially in identifying protein coding genes, our understanding of the genome is far from complete, particularly with regard to noncoding RNAs, alternatively spliced transcripts, and regulatory sequences. Systematic analyses of transcripts and regulatory information are essential to the identification of genes and regulatory regions and are an important resource for the study of human biology and disease. Such analyses can also provide comprehensive views of the organization and variability of genes and regulatory information across cellular contexts, species and individuals.

The Encyclopedia of DNA Elements (ENCODE) Project aims to delineate all functional elements encoded in the human genome13. Operationally, we define a functional element as a discrete genome segment that encodes a defined product (e.g., protein or non-coding RNA) or displays a reproducible biochemical signature (e.g., protein-binding, or a specific chromatin structure). Comparative genomic studies suggest that 3–8% of bases are under purifying (negative) selection 48 and therefore may be functional, although other analyses have suggested much higher estimates 911. In a pilot phase covering 1% of the genome, the ENCODE project annotated 60% of mammalian evolutionarily constrained bases, but also identified many additional putative functional elements without evidence of constraint2. The advent of more powerful DNA sequencing technologies now enables whole genome and more precise analyses with a broad repertoire of functional assays.

Here, we describe production and initial analysis of 1,640 datasets designed to annotate functional elements in the entire human genome. We integrate results from diverse experiments within cell types, related experiments involving 147 different cell types, and all ENCODE data with other resources, such as candidate regions from genome-wide association studies (GWAS) and evolutionarily constrained regions. Together, these efforts reveal important features about the organization and function of the human genome, including:

  1. The vast majority (80.4%) of the human genome participates in at least one biochemical RNA and/or chromatin associated event in at least one cell type. Much of the genome lies close to a regulatory event: 95% of the genome lies within 8kb of a DNA-protein interaction (as assayed by bound ChIP-seq motifs or DNaseI footprints), and 99% is within 1.7kb of at least one of the biochemical events measured by ENCODE.
  2. Primate-specific elements as well as elements without detectable mammalian constraint show, in aggregate, evidence of negative selection; thus some of them are expected to be functional.
  3. Classifying the genome into seven chromatin states suggests an initial set of 399,124 regions with enhancer-like features and 70,292 regions with promoter-like features, as well hundreds of thousands of quiescent regions. High-resolution analyses further subdivide the genome into thousands of narrow states with distinct functional properties.
  4. It is possible to quantitatively correlate RNA sequence production and processing with both chromatin marks and transcription factor (TF) binding at promoters, indicating that promoter functionality can explain the majority of RNA expression variation.
  5. Many non-coding variants in individual genome sequences lie in ENCODE-annotated functional regions; this number is at least as large as those that lie in protein coding genes.
  6. SNPs associated with disease by GWAS are enriched within non-coding functional elements, with a majority residing in or near ENCODE-defined regions that are outside of protein coding genes. In many cases, the disease phenotypes can be associated with a specific cell type or TF.

ENCODE data production and initial analyses

Since 2007, ENCODE has developed methods and performed a large number of sequence-based studies to map functional elements across the human genome3. The elements mapped (and approaches used) include RNA transcribed regions (RNA-seq, CAGE, RNA-PET, and manual annotation), protein-coding regions (mass spectrometry), TF-binding sites (ChIP-seq and DNase-seq), chromatin structure (DNase-seq, FAIRE-seq, histone ChIP-seq and MNase-seq), and DNA methylation sites (RRBS assay) (Box 1 itemizes methods and abbreviations, Supplementary Table P1 details production statistics)3. To compare and integrate results across the different laboratories, data production efforts focused on two selected sets of cell lines, designated “Tier 1” and “Tier 2” (Box 1). To capture a broader spectrum of biological diversity, selected assays were also executed on a third tier comprising more than 100 cell types including primary cells. All data and protocol descriptions are available at http://www.encodeproject.org/, and a “User’s Guide” including details of cell type choice and limitations was recently published3.

Box 1

RNA-seqIsolation of RNA sequences, often with different purification techniques to isolate different fractions of RNA followed by high-throughput sequencing
CAGECapture of the methylated cap at the 5′ end of RNA, followed by high- throughput sequencing of a small tag adjacent to the 5′ methylated caps. 5′ methylated caps are formed at the initiationof transcription, though other mechanisms also methylate 5′ ends of RNA
RNA-PETSimultaneous capture of RNAs with both a 5′ methyl cap and a poly-A tail, which is indicative of a full-length RNA. This is then followed by sequencing a short tag from each end by high- throughput sequencing
ChIP-seqChromatin Immunoprecipitation followed by sequencing. Specific regions of cross-linked chromatin, which is genomic DNA complexed with its bound proteins, are selected by using an antibody to a specific epitope. The enriched sample is then subjected to high-throughput sequencing to determine the regions in the genome most often bound by the protein to which the antibody was directed. Most often used are antibodies to any chromatin-associated epitope, including transcription factors, chromatin binding proteins, and specific chemical modifications on histone proteins.
DNaseI-seqAdaption of established regulatory sequence assay to modern techniques. The DNaseI enzyme will preferentially cut live chromatin preparations at sites where nearby there are specific (non-histone) proteins. The resulting cut points are then sequenced using high throughput sequencing to determine those sites “hypersensitive” to DNaseI, corresponding to open chromatin.
FAIRE-seqFormaldehyde Assisted Isolation of Regulatory Elements. FAIRE isolates nucleosome-depleted genomic regions by exploiting the difference in crosslinking efficiency between nucleosomes (high) and sequence-specific regulatory factors (low). FAIRE consists of crosslinking, phenol extraction, and sequencing the DNA fragments in the aqueous phase.
RRBSReduced Representation Bisulfite Sequencing. Bisulfite treatment of DNA sequence converts methylated cytosines to uracil. In order to focus the assay and save costs, specific restriction enzymes that cut around CpG dinucleotides can reduce the genome to a portion specifically enriched in CpGs. This enriched sample is then sequenced to quantitatively determine the methylation status of individual cytosines.
Tier 1Tier 1 cell types were the highest-priority set and comprised three widely-studied cell lines: K562 erythroleukemia cells; GM12878, a B-lymphoblastoid cell line that is also part of the 1,000 Genomes project (http://1000genomes.org)55; and the H1 embryonic stem cell (H1 hESC) line.
Tier 2The second-priority set of cell types in the ENCODE project which included HeLa-S3 cervical carcinoma cells, HepG2 hepatoblastoma cells, and primary (non-transformed) human umbilical vein endothelial cells (HUVEC).
Tier 3Any other ENCODE cell types not in Tier 1 or Tier 2.

Integration methodology

For consistency, data were generated and processed using standardized guidelines, and for some assays, new quality-control measures were designed (see refs 3,12, http://encodeproject.org/ENCODE/dataStandards.html and Kundaje, A. Personal Communication). Uniform data-processing methods were developed for each assay (see Supplementary Information and Kundaje, A. Personal Communication), and most assay results can be represented both as signal information, a per-base estimate across the genome and as discrete elements, regions computationally identified as enriched for signal. Extensive processing pipelines were developed to generate each representation (M.M. Hoffman et al., manuscript in preparation, Kundaje, A. Personal Communication). In addition we developed the irreproducible discovery rate (IDR)13 measure to provide a robust and conservative estimate of the threshold where two ranked lists of results from biological replicates no longer agree (i.e., are irreproducible) and we applied this to defining sets of discrete elements. We identified, and excluded from most analyses, regions yielding untrustworthy signals likely to be artifactual (e.g., multi-copy regions). Together, these regions comprise 0.39% of the genome (see Supplementary Information). The accompanying poster represents different ENCODE-identified elements and their genome coverage.

Transcribed and protein-coding regions

We used manual and automated annotation to produce a comprehensive catalogue of human protein-coding and non-coding RNAs as well as pseudogenes, referred to as the GENCODE reference gene set14,15 (Supplementary Table U1). This includes 20,687 protein-coding genes (GENCODE annotation, V7), with on average 6.3 alternatively spliced transcripts (3.9 different protein-coding transcripts) per locus. In total GENCODE annotated exons of protein coding genes cover 2.94% of the genome or 1.22% for protein-coding exons. Protein-coding genes span 33.45% from the outermost start to stop codons, or 39.54% from promoter to poly A site. Analysis of mass spectrometry (MS) data from K562 and GM12878 cell lines yielded 57 confidently-identified unique peptide sequences intergenic relative to GENCODE annotation. Taken together with evidence of pervasive genome transcription16, these data indicate that additional protein–coding genes remain to be found.

In addition, we annotated 8,801 automatically derived small RNAs and 9,640 manually curated long non-coding RNA (lncRNA) loci 17. Comparing lncRNAs to other ENCODE data indicates that lncRNAs are generated through a pathway similar to that for protein coding genes17. The GENCODE project also annotated 11,224 pseudogenes, of which 863 were transcribed and associated with active chromatin18.


We sequenced RNA16 from different cell lines and multiple subcellular fractions to develop an extensive RNA expression catalogue. Using a conservative threshold to identify regions of RNA activity, 62% of genomic bases are reproducibly represented in sequenced long (>200 nucleotides) RNA molecules or GENCODE exons. Of these bases, only 5.5% are explained by GENCODE exons. The majority of transcribed bases are within or overlapping annotated genes boundaries (i.e. intronic) and only31% of bases in sequenced transcripts were intergenic16.

We used CAGE-seq (5′ cap-targeted RNA isolation and sequencing) to identify 62,403 transcription start sites (TSSs) at high confidence (IDR of 0.01) in Tier 1 and 2 cell types. Of these, 27,362 (44%) are within 100 bp of the 5′ end of a GENCODE-annotated transcript or previously reported full-length mRNA. The remaining regions predominantly lie across exons and 3′ UTRs, and some exhibit cell type restricted expression; these may represent the start sites of novel, cell type-specific transcripts.

Finally, we saw a significant proportion of coding and non-coding transcripts processed into steady state stable RNAs shorter than 200 nucleotides. These precursors include t-, mi-, sn- and sno-RNAs and the 5′ termini of these processed products align with the capped 5′ end tags16.

Regions bound by transcription factors, transcriptional machinery, and other proteins

To directly identify regulatory regions, we mapped the binding locations of 119 different DNA-binding proteins and a number of RNA polymerase components in 72 cell types using ChIP-seq (Table 1, Supplementary Table N1, ref 19); 87 (73%) were sequence-specific TFs (TFSS). Overall, 636,336 binding regions covering 231Mb (8.1%) of the genome are enriched for regions bound by DNA-binding proteins across all cell types. We assessed each protein-binding site for enrichment of known DNA-binding motifs and the presence of novel motifs. Overall, 86% of the DNA segments occupied by TFSS contained a strong DNA-binding motif and in most (55%) cases, the known motif was most enriched (Pouya Kheradpour and Manolis Kellis, personal communication).

Table 1
Summary of TF classes analysed in ENCODE.

Protein-binding regions lacking high or moderate affinity cognate recognition sites have 21% lower median scores by rank than regions with recognition sequences (Wilcoxon rank sum p-value < 10−16). 82% of the low-signal regions have high-affinity recognition sequences for other factors. In addition, when ChIP-seq peaks are ranked by their concordance with their known recognition sequence, the median DNase I accessibility is two-fold higher in the bottom 20% of peaks than in the upper 80% (Genome Structure Correction20, GSC p-value <10−16) consistent with previous observations2124. We speculate that low signal regions are either lower-affinity sites21 or indirect TF target regions associated through interactions with other factors (see also refs 25,26).

We organized all the information associated with each TF, including the ChIP-seq peaks, discovered motifs, and associated histone modification patterns, in FactorBook (http://www.factorbook.org, 26), a public resource which will be updated as the project proceeds.

DNaseI hypersensitive sites, footprints and nucleosome-depleted regions

Chromatin accessibility characterized by DNaseI hypersensitivity is the hallmark of regulatory DNA regions27,28. We mapped 2.89 million unique, non-overlapping DNaseI hypersensitive sites (DHSs) by DNase-seq in 125 cell types, the overwhelming majority of which lie distal to TSSs 29. We also mapped 4.8 million sites across 25 cell types that displayed reduced nucleosomal crosslinking by FAIRE, many of which coincide with DHSs. In addition, we used micrococcal nuclease to map nucleosome occupancy in GM12878 and K562 cells 30.

In Tier 1 and Tier 2 cell types, we identified a mean of 205,109 DHSs per cell type (at FDR 1%), encompassing an average of 1.0% of the genomic sequence in each cell type, and 3.9% in aggregate. On average, 98.5% of the occupancy sites of TFs mapped by ENCODE ChIP-seq (and, collectively, 94.4% of all 1.1 million TF ChIP-seq peaks in K562) lay within accessible chromatin defined by DNaseI hotspots29. However, a small number of factors, most prominently heterochromatin-bound repressive complexes (e.g., the Kap1-SetDB1-Znf274 complex31,32 encoded by the TRIM28, SETDB1 and ZNF274 genes), appear to occupy a significant fraction of nucleosomal sites.

Using genomic DNaseI footprinting33,34 on 41 cell types we identified 8.4 million distinct DNaseI footprints (FDR 1%)25. Our de novo motif discovery on DNaseI footprints recovered ~90% of known TF motifs, together with hundreds of novel evolutionarily conserved motifs, many displaying highly cell-selective occupancy patterns similar to major developmental and tissue-specific regulators.

Regions of histone modifications

We assayed chromosomal locations for up to 12 histone modifications and variants in 46 cell types, including a complete matrix of eight modifications across Tier 1 and Tier 2. Because modification states may span multiple nucleosomes, which themselves can vary in position across cell populations, we used a continuous signal measure of histone modifications in downstream analysis, rather than calling regions (M.M. Hoffman et al., manuscript in preparation, http://code.google.com/p/align2rawsignal/). For the strongest, “peak-like” histone modifications, we used MACS 35 to characterize enriched sites. Table 2 describes the different histone modifications, their peak characteristics, and a summary of their known roles (reviewed in refs3639).

Table 2
Summary of histone modifications and variants studied in ENCODE, their peak characteristics, and putative functions.

Our data show that global patterns of modification are highly variable across cell types, in accordance with changes in transcriptional activity. Consistent with prior studies40,41, we find that integration of the different histone modification information can be used systematically to assign functional attributes to genomic regions (see below).

DNA methylation

Methylation of cytosine, usually at CpG dinucleotides, is involved in epigenetic regulation of gene expression. Promoter methylation is typically associated with repression, whereas genic methylation correlates with transcriptional activity42. We used reduced representation bisulfite sequencing (RRBS) to quantitatively profile DNA methylation for an average of 1.2 million CpGs in each of 82 cell lines and tissues (8.6% of non-repetitive genomic CpGs), including CpGs in intergenic regions, proximal promoters, and in intragenic regions (gene bodies)43, although it should be noted that the RRBS method preferentially targets CpG rich islands. We found 96% of CpGs exhibited differential methylation in at least one cell type or tissue assayed (K. Varley et al. Personal Communication), and levels of DNA methylation correlated with chromatin accessibility. The most variably methylated CpGs are found more often in gene bodies and intergenic regions, rather than in promoters and upstream regulatory regions. In addition, we identified an unexpected correspondence between unmethylated genic CpG islands and binding by P300, a histone acetyltransferase linked to enhancer activity44.

Because RRBS is a sequence-based assay with single-base resolution, we were able to identify CpGs with allele-specific methylation consistent with genomic imprinting, and determined that these loci exhibit aberrant methylation in cancer cell lines (K. Varley et al. Personal Communication). Furthermore, we detected reproducible cytosine methylation outside CpG dinucleotides in adult tissues45, providing further support that this non-canonical methylation event may play important roles in human biology (K. Varley et al. Personal Communication).

Chromosome-interacting regions

Physical interaction between distinct chromosome regions that can be separated by hundreds of kb is thought to be important in the regulation of gene expression 46. We used two complementary chromosome conformation capture (3C)-based technologies to probe these long-range physical interactions.

A 3C-carbon copy (5C) approach47,48 provided unbiased detection of long-range interactions with TSSs in a targeted 1% of the genome (the 44 ENCODE pilot regions) in four cell types (GM12878, K562, HeLa-S3, and H1hESC)49. We discovered hundreds of statistically significant long-range interactions in each cell type after accounting for chromatin polymer behavior and experimental variation. Pairs of interacting loci showed strong correlation between the gene expression level of the TSS and the presence of specific functional element classes such as enhancers. The average number of distal elements interacting with a TSS was 3.9, and the average number of TSSs interacting with a distal element was 2.5, indicating a complex network of interconnected chromatin. Such interwoven long-range architecture was also uncovered genome-wide using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET)50 applied to identify interactions in chromatin enriched by RNA polymerase II (PolII) ChIP from five cell types51. In K562 cells, we identified 127,417 promoter-centered chromatin interactions using ChIA-PET, 98% of which were intra-chromosomal. While promoter regions of 2,324 genes were involved in “single-gene” enhancer-promoter interactions, those of 19,813 genes were involved in “multi-gene” interaction complexes spanning up to several megabases, including promoter-promoter and enhancer-promoter interactions51.

These analyses portraya complex landscape of long-range gene-element connectivity across ranges of hundreds of kb to several Mb, including interactions among unrelated genes (Supplementary Figure Y1). Furthermore, in the 5C results, 50–60% of long-range interactions occurred in only one of the four cell lines, indicative of a high degree of tissue specificity for gene-element connectivity 49.

Summary of ENCODE-identified elements

Accounting for all these elements, a surprisingly large amount of the human genome, 80.4%, is covered by at least one ENCODE-identified element (detailed in Supplementary Table Q1). The broadest element class represents the different RNA types covering 62% of the genome (although the majority is inside of introns or near genes). Regions highly enriched for histone modifications form the next largest class (56.1%). Excluding RNA elements and broad histone elements 44.2 % of the genome is covered. Smaller proportions of the genome are occupied by regions of open chromatin (15.2%) or sites of TF binding (8.1%), with 19.4% covered by at least one DHS or TF ChIP-seq peak across all cell lines. Using our most conservative assessment, 8.5% of bases are covered by either a TF binding site motif (4.6%) or a DHS footprint (5.7%). This however is still about 4.5-fold higher than the amount of protein coding exons, and about 2-fold higher than the estimated amount of pan-mammalian constraint.

Given that ENCODE did not assay all cell types, or all TFs, and in particular has sampled few specialized or developmentally restricted cell lineages, these proportions must be underestimates of the total amount of functional bases. However, many assays were performed on more than one cell type, allowing assessment of the rate of discovery of new elements. For both DHSs and CTCF sites, the number of new elements initially increases rapidly with a steep gradient for the saturation curve and then slows with increasing numbers of cell types (Supplementary Figure R1 and R2). With the current data, at the flattest part of the saturation curve, each new cell type adds on average 9,500 DHS elements (across 106 cell types) and 500 CTCF-binding elements (across 49 cell types), representing 0.45% of the total element number. We modelled saturation for the DHSs and CTCF-binding sites using a Weibull distribution (r2 > 0.999) and predict saturation at approximately 4.1 million (S.E. = 108,000) and 185,100 (S.E. = 18,020) sites, respectively, suggesting that we have discovered around half of the estimated total DHSs. These estimates represent a lower bound, but reinforce the observation that there is more non-coding functional DNA than either coding sequence or pan-mammalian constraint.

The impact of selection on functional elements

From comparative genomic studies, at least 3–8% of bases are under purifying (negative) selection 411 indicating that these bases may potentially be functional. We previously found that 60% of mammalian evolutionarily constrained bases were annotated in the ENCODE pilot project, but also observed that many functional elements lacked evidence of constraint2, a conclusion substantiated by others5254. The diversity and genome-wide occurrence of functional elements now identified provides an unprecedented opportunity to further examine the forces of negative selection on human functional sequences.

We examined negative selection using two measures that highlight different periods of selection in the human genome. The first measure, inter-species, pan-mammalian constraint (GERP-based scores; 24 mammals8) addresses selection during mammalian evolution. The second measure is intra-species constraint estimated from the numbers of variants discovered in human populations using data from the 1000 Genomes project55 and covers selection over human evolution. In Figure 1, we plot both these measures of constraint for different classes of identified functional elements, excluding features overlapping exons and promoters that are known to be constrained. Each graph also shows genomic background levels and measures of coding-gene constraint for comparison. Since we plot human population diversity on an inverted scale, elements that are more constrained by negative selection will tend to lie in the upper and right hand regions of the plot.

Figure 1
Impact of Selection on ENCODE Functional Elements in Mammals and Human Populations

For DNaseI elements (Figure 1B) and bound motifs (Figure 1C) most sets of elements show enrichment in pan mammalian constraint and decreased human population diversity, though for some cell types the DNaseI sites do not appear overall to be subject to pan-mammalian constraint. Bound TF motifs have a natural control from the set of TF motif with equal sequence potential for binding but without binding evidence from ChIP-seq experiments; in all cases, the bound motifs show both more mammalian constraint and higher suppression of human diversity.

Consistent with previous findings, we do not observe genome-wide evidence for pan-mammalian selection of novel RNA sequences (Panel D). There are also a large number of elements without mammalian constraint, between 17–90% for TF-binding regions as well as DHSs and FAIRE regions. Previous studies could not determine whether these sequences are either biochemically active, but with little overall impact on the organism, or are under lineage specific selection. By isolating sequences preferentially inserted into the primate lineage, which is only feasible given the genome-wide scale of this data, we are able to specifically examine this issue. The majority of primate-specific sequence is due to retrotransposon activity, but an appreciable proportion is non-repetitive primate-specific sequence. Of 104,343,413 primate-specific bases (excluding repetitive elements), 67,769,372 (65%) are found within ENCODE-identified elements. Examination of 227,688 variants segregating in these primate specific regions revealed that all classes of elements (RNA and regulatory) show depressed derived allele frequencies, consistent with recent negative selection occurring in at least some of these regions (Figure 1E). An alternative approach examining sequences that are not clearly under pan-mammalian constraint showed a similar result (Luke Ward and Manolis Kellis, personal communication). This suggests that an appreciable proportion of the unconstrained elements are lineage specific elements required for organismal function, consistent with long standing views of recent evolution56, and the remainder are likely to be “neutral” elements2 which are not currently under selection, but may still affect cellular or larger scale phenotypes without an effect on fitness.

The binding patterns of TFs are not uniform, and we can correlate both inter-and intra-species measures of negative selection with the overall information content of motif positions. The selection on some motif positions is as high as protein coding exons (Figure 1F, Luke Ward and Manolis Kellis, personal communication). These aggregate measures across motifs show that the binding preferences found in the population of sites are also relevant to the per-site behavior. By developing a per-site metric of population effect on bound motifs, we found that highly constrained bound instances across mammals are able to buffer the impact of individual variation57.

Integration of ENCODE data with known genomic features

Promoter-anchored integration

Many of the ENCODE assays directly or indirectly provide information about the action of promoters. Focusing on the TSSs of protein-coding transcripts, we investigated the relationships among different ENCODE assays, in particular testing the hypothesis that RNA expression (“output”) can be effectively predicted from patterns of chromatin modifications or TF binding (“input”). Consistent with previous reports58, we observe two relatively distinct types of promoters: (1) broad, mainly C+G rich, TATA-less promoters; and (2) narrow, TATA-box-containing promoters. These promoters have distinct patterns of histone modifications, and TF-binding sites are selectively enriched in each class (Supplementary Figure Z1).

We developed predictive models to explore the interaction between histone modifications and measures of transcription at promoters, distinguishing between modifications known to be added as a consequence of transcription (such as H3K36me3 and H3K79me2) and other categories of histone marks59. In our analyses, the best models had two components: an initial classification component (on/off) and a second quantitative model component. Our models showed activating acetylation marks (H3K27ac and H3K9ac) are roughly as informative as activating methylation marks (H3K4me3 and H3K4me2) (Figure 2A). Although repressive marks, such as H3K27me3 or H3K9me3, show negative correlation both individually and in the model, removing these marks produces only a small reduction in model performance. However, for a subset of promoters in each cell line repressive histone marks (H3K27me3 or H3K9me3) must be used to accurately predict their expression. We also examined the interplay between the H3K79me2 and H3K36me3 marks, both of which mark gene bodies, likely reflecting recruitment of modification enzymes by polymerase isoforms. As described previously, H3K79me2 occurs preferentially at the 5′ ends of gene bodies and H3K36me3 occurs more 3′, and our analyses support the previous model in which the H3K79me2 to H3K36me3 transition occurs at the first 3′ splice site60.

Figure 2
Modelling Transcription Levels from Histone Modification and TF-Binding Patterns

Few previous studies have attempted to build qualitative or quantitative models of transcription genome-wide from TF levels because of the paucity of documented TF-binding regions and the lack of coordination around a single cell line. We thus examined the predictive capacity of TF-binding signals for the expression levels of promoters (Figure 2B). In contrast to the profiles of histone modifications, most TFs show enriched binding signals in a narrow DNA region near the TSS, with relatively higher binding signals in promoters with higher CpG content. Most of this correlation could be recapitulated by looking at the aggregate binding of TFs without specific TF terms. Together, these correlation models suggest both that a limited set of chromatin marks are sufficient to “explain” transcription and that a variety of TFs might have broad roles in general transcription levels across many genes. It is important to note that this is an inherently observational study of correlation patterns, and is consistent with a variety of mechanistic models with different causal links between the chromatin, TF and RNA assays. However it does indicate that there is enough information present at the promoter regions of genes to explain the majority of variation in RNA expression.

We developed predictive models similar to those used to model transcriptional activity to explore the relationship between levels of histone modifications and inclusion of exons in alternately spliced transcripts. Even accounting for expression level, H3K36me3 has a positive contribution to exon inclusion, while H3K79me2 has a negative contribution61. By monitoring the RNA populations in the subcellular fractions of K562 cells, we found that essentially all splicing is co-transcriptional 62, further supporting a link between chromatin structure and splicing.

Transcription factor-binding site-anchored integration

TF binding sites provide a natural focus around which to explore chromatin properties. TFs are often multi-functional and can bind a variety of genomic loci with different combinations and patterns of chromatin marks and nucleosome organization. Hence, rather than averaging chromatin mark profiles across all binding sites of a TF, we developed a clustering procedure, termed the Clustered Aggregation Tool (CAGT), to identify subsets of binding sites sharing similar but distinct patterns of chromatin mark signal magnitude, shape, and hidden directionality 30. For example, the average profile of the repressive histone mark, H3K27me3, over all 55,782 CTCF-binding sites in K562 shows poor signal enrichment (Figure 3A). However, after grouping profiles by signal magnitude, we found a subset of 9,840 (17.6%) CTCF-binding sites that exhibit significant flanking H3K27me3 signal. Shape and orientation analysis further revealed that the predominant signal profile for H3K27me3 around CTCF peak summits is asymmetric, consistent with a boundary role for some CTCF sites between active and polycomb-silenced domains. Further examples are provided in Supplementary Figures E5 and E6. For TAF1, predominantly found near TSSs, the asymmetric sites are orientated with the direction of transcription. However, for distal sites, such as those bound by GATA1 and CTCF, we also observed a high proportion of asymmetric histone patterns, although independent of motif directionality. In fact, all TF-binding datasets in all cell lines show predominantly asymmetric patterns (asymmetry ratio >0.6) for all chromatin marks but not DNaseI (Figure 3B). This suggests that most TF bound chromatin events correlate with structured, directional patterns of histone modifications, and that promoter directionality is not the only source of orientation at these sites.

Figure 3
Patterns and Asymmetry of Chromatin Modification at Transcription Factor-binding Sites

We also examined nucleosome occupancy relative to the symmetry properties of chromatin marks around TF-binding sites. Around TSSs, there is usually strong asymmetric nucleosome occupancy, often accounting for the majority of the histone modification signal (for instance, see Supplementary Figure E4). However, away from TSSs, there is far less concordance. For example, CTCF-binding sites typically show arrays of well-positioned nucleosomes on either side of the peak summit (Supplementary Figure E1)63. Where the flanking chromatin mark signal is high, the signals are often asymmetric, indicating differential marking with histone modifications (Supplementary Figure E2 and E3). Thus, we confirm on a genome-wide scale that TFs can form barriers around which nucleosomes and histone modifications are arranged in a variety of configurations6366. Further detail is explored in refs25,26,30.

Transcription factor co-associations

TF-binding regions are non-randomly distributed across the genome, with respect to both other features (e.g., promoters) and other TF-binding regions. Within the Tier 1 and 2 cell lines, we found 3,307 pairs of statistically co-associated factors (P value < 1E-16, GSC) involving 114 out of a possible 117 factors (97%) (Figure 4A). These include expected associations, such as Jun and Fos, and some more novel associations, such as TCF7L2 with HNF4alpha and FoxA267 (a full listing is given in Supplementary Table F1). When one considers promoter and intergenic regions separately, this changes to 3,201 pairs (116 factors, 99%) for promoters and 1,564 pairs (108 factors, 92%) for intergenic regions, with some associations more specific to these genomic contexts (e.g., the cluster of HDAC2, GABPA, CHD2, GTF2F1, MXI1, and MYC in promoter regions and SP1, EP300, HDAC2, and NANOG in intergenic regions (Figure 4B)). These general and context-dependent associations lead to a network representation of the co-binding with many interesting properties, explored in refs 19,25,26. In addition we also identified a set of regions bound by multiple factors representing “High Occupancy of TFs” (HOT) regions68.

Figure 4
Co-association between Transcription Factors

Genome-wide integration

To identify functional regions genome-wide, we next integrated elements independent of genomic landmarks using either discriminative training methods, where a subset of known elements of a particular class were used to train a model that was then used to discover more instances of this class, or using methods in which only data from ENCODE assays were employed without explicit knowledge of any annotation.

For discriminative training, we used a three-step process to predict potential enhancers, described in Supplementary Info and ref 68. Two alternative discriminative models converged on a set of ~13,000 putative enhancers in K562 cells68. In the second approach, two methodologically distinct unbiased approaches (see ref 40,69 and M.M. Hoffman et al., manuscript in preparation) converged on a concordant set of histone modification and chromatin-accessibility patterns that can be used to segment the genome in each of the Tier 1 and Tier 2 cell lines, although the individual loci in each state in each cell line are different. With the exception of RNA polymerase II and CTCF, the addition of TF data did not substantially alter these patterns. At this stage, we deliberately excluded RNA and methylation assays, reserving these data as a means to validate the segmentations.

Our integration of the two segmentation methods (M.M. Hoffman et al., manuscript in preparation) established a consensus set of seven major classes of genome states, described in Table 3. The standard view of active promoters, with a distinct core promoter region (TSS and PF states), leading to active gene bodies (T, transcribed state) is rediscovered in this model (Figure 5A and B). There are three “active” distal states. We tentatively labelled two as enhancers (predicted enhancers, E, and predicted weak enhancers, WE) due to their occurrence in regions of open chromatin with high H3K4me1, although they differ in the levels of marks such as H3K27ac, currently thought to distinguish active from inactive enhancers. The other active state (CTCF) has high CTCF binding and includes sequences that function as insulators in a transfection assay. The remaining repressed state (R) summarises sequences split between different classes of actively repressed or inactive, quiescent chromatin. We found that the CTCF-binding associated state is relatively invariant across cell types, with individual regions frequently occupying the CTCF state across all six cell types (Figure 5C). Conversely, the E and T states have substantial cell-specific behaviour, whereas the TSS state has a bimodal behaviour with similar numbers of cell-invariant and cell-specific occurrences. It is important to note that the consensus summary classes do not capture all the detail discovered in the individual segmentations containing more states.

Figure 5
Integration of ENCODE Data by Genome-wide Segmentation
Table 3
Summary of the combined state types.

The distribution of RNA species across segments is quite distinct, indicating that underlying biological activities are captured in the segmentation. Polyadenylated RNA is heavily enriched in gene bodies. Around promoters, there are short RNA species previously identified as promoter-associated short RNAs (PASRs) (Figure 5B)16,70. Similarly, DNA methylation shows marked distinctions between segments, recapitulating the known biology of predominantly unmethylated active promoters (TSS states) followed by methylated gene bodies42 (T state, Figure 5D). The two enhancer-enriched states show distinct patterns of DNA methylation, with the less active enhancer state (by H3K27ac/H3K4me1 levels) showing higher methylation. These states also have an excess of RNA elements without poly-A tails and methyl-cap RNA as assayed by CAGE sequences compared to matched intergenic controls, suggesting a specific transcriptional mode associated with active enhancers71. TFs also showed distinct distributions across the segments (Figure 5B). A striking pattern is the concentration of TFs in the TSS-associated state. The enhancers contain a different set of TFs. For example, in K562, the E state is enriched for binding by the proteins encoded by the EP300, FOS, FOSL1, GATA2, HDAC8, JUNB, JUND, NFE2, SMARCA4, SMARCB1, SIRT6, and TAL1 genes. We tested a subset of these predicted enhancers in both Mouse and Fish transgenic models (examples in Figure 6), with over half of the elements showing activity, often in the corresponding tissue type.

Figure 6
Experimental Characterisation of Segmentations

The segmentation provides a linear determination of functional state across the genome, but not an association of particular distal regions with genes. By using the variation of DNaseI across cell lines, 39% of E (enhancer associated) states could be linked to a proposed regulated gene 29 concordant with physical proximity patterns determined by 5C49 or ChIA-PET.

To provide a fine-grained regional classification, we turned to a Self Organizing Map (SOM) to cluster genome segmentation regions based on their assay signal characteristics (Figure 7). The segmentation regions were initially randomly assigned to a 1,350-state map in a two-dimensional toroidal space (Figure 7A). This map can be visualised as a two dimensional rectangular plane onto which the various signal distributions can be plotted. For instance, the rectangle at the bottom left of Figure 7A shows the distribution of the genome in the initial randomised map. The SOM was then trained using the 12 different ChIP-seq and DNase-seq assays in the six cell types previously analyzed in the large-scale segmentations (i.e. over 72-dimensional space). After training, the SOM clustering was again visualised in two dimensions, now showing the organized distribution of genome segments (lower right hand, Figure 7A). Individual data sets associated with the genome segments in each SOM map unit (hexagonal cells) can then be visualised in the same framework to learn how each additional kind of data is distributed on the chromatin state map. Figure 7B shows CAGE/TSS expression data overlaid on the randomly initialised (left) and trained map (right) panels. In this way the trained SOM highlighted cell type-specific TSS clusters (bottom panels of Figure 7B), indicating that there are sets of tissue specific TSSs that are distinguished from each other by subtle combinations of ENCODE chromatin data. Many of the ultra-fine-grained state classifications revealed in the SOM are associated with specific gene ontology (GO) terms (right panel of Figure 7C). For instance, the left panel of Figure 7C, identifies 10 SOM map units enriched with genomic regions associated with genes associated with the GO term ‘immune response’. The central panel identifies a different set of map units enriched for the GO term “sequence-specific TF activity”. The two map units most enriched for this GO term, indicated by the darkest green colouring, contain genes with segments that are high in H3K27me3 in H1 hESC cells, but that differ in H3K27me3 levels in HUVEC cells. Gene function analysis with the GO ontology tool (GREAT72) reveals that the map unit with high H3K27me3 in both cell types is enriched in TF genes with known neuronal functions, whereas the neighbouring map unit is enriched in genes involved in body patterning. The genome browser shots at the bottom of Figure 7C pick out an example region for each of the two SOM map units illustrating the difference in H3K27me3 signal. Overall, we have 228 distinct GO terms associated with specific segments across one or more states (Ali Mortazavi, personal communication), and can assign over one third of genes to a GO annotation solely on the basis of its multi-cellular histone patterns. Thus the SOM analysis provides a fine-grained map of chromatin data across multiple cell types, which can then be used to relate chromatin structure to other data-types at differing levels of resolution (for instance, the large cluster of units containing any active TSS, its sub-clusters composed of units enriched in TSSs active in only one cell type, or individual map units significantly enriched for specific GO terms).

Figure 7
High-Resolution Segmentation of ENCODE Data by Self-Organising Maps (SOM)

The classifications presented here are necessarily limited by the assays and cell lines studied, and are likely to contain a number of heterogeneous classes of elements. Nonetheless, robust classifications can be made, allowing a systematic view of the human genome.

Insights into human genomic variation

We next explored the potential impact of sequence variation on ENCODE functional elements. We examined allele-specific variation using results from the GM12878 cells that are derived from an individual (NA12878) sequenced in the 1000 Genomes project, along with her parents. Since ENCODE assays are predominantly sequence-based, the trio design allows each GM12878 dataset to be divided by the specific parental contributions at heterozygous sites, producing aggregate haplotypic signals from multiple genomic sites. We examined 193 ENCODE assays for allele-specific biases using 1,409,992 phased, heterozygous SNPs and 167,096 indels (Figure 8). Alignment biases towards alleles present in the reference genome sequence were avoided utilising a sequence specifically tailored to the variants and haplotypes present in NA12878 (a ‘personalised genome’)73. We found instances of preferential binding towards each parental allele. For example, comparison of the results from the POLR2A, H3K79me2, and H3K27me3 assays in the region of NACC2 (Figure 8A) shows a strong paternal bias for H3K79me2 and POL2RA and a strong maternal bias for H3K27me3, suggesting differential activity for the maternal and paternal alleles.

Figure 8
Allele-Specific ENCODE Elements

Figure 8B shows the correlation of selected allele-specific signals across the whole genome. For instance we find a strong allelic correlation between POL2RA and BCLAF1 binding, as well as negative correlation between H3K79me2 and H3K27me3, both at genes (below the diagonal, bottom left) and chromosomal segments (top right). Overall we find that positive allelic correlations among the 193 ENCODE assays are stronger and more frequent than negative correlations. This may be due to preferential capture of accessible alleles and/or the specific histone modification and TF, assays used in the project.

Rare variants, individual genomes and somatic variants

We further investigated the potential functional effects of individual variation in the context of ENCODE annotations. We divided NA12878 variants into common and rare classes, and partitioned these into those overlapping ENCODE annotation (Figure 9A, Supplementary Tables K1 and K2). We also predicted potential functional effects: for protein-coding genes, these are either non-synonymous SNPs or variants likely to induce loss of function by frame-shift, premature stop, or splice-site disruption; for other regions, these are variants that overlap a TF-binding site. We found similar numbers of potentially functional variants affecting protein-coding genes or affecting other ENCODE annotations, suggesting that many functional variants within individual genomes lie outside exons of protein-coding genes. A more detailed analysis of regulatory variant annotation is described in ref 74.

Figure 9
Examining ENCODE Elements on a per individual basis in the Normal and Cancer Genome

To further study the potential effects of NA12878 genome variants on TF binding regions, we performed peak-calling using a constructed personal diploid genome sequence for NA1287873. We aligned ChIP-seq sequences from GM12878 separately against the maternal and paternal haplotypes. As expected, a greater fraction of reads were aligned than to the reference genome (see Supplementary Information, Supplementary Figure K1). On average, approximately 1% of TF-binding sites in GM12878 are detected in a haplotype-specific fashion. For instance, Figure 9B shows a CTCF-binding site not detected using the reference sequence that is only present on the paternal haplotype due to a 1-bp deletion (see also Supplementary Figure K2). As costs of DNA sequencing decrease further, optimized analysis of ENCODE-type data should use the genome sequence of the individual or cell being analyzed when possible.

Most analyses of cancer genomes to date have focused on characterizing somatic variants in protein-coding regions. We intersected four available whole-genome cancer datasets with ENCODE annotations (Figure 9C, Supplementary Figure L2). Overall somatic variation is relatively depleted from ENCODE annotated regions, particularly for elements specific to a cell type matching the putative tumor source (e.g., skin melanocytes for melanoma). Examining the mutational spectrum of elements in introns for cases where a strand-specific mutation assignment could be made reveals that there are mutational spectrum differences between DHSs and unannotated regions (0.06 Fisher’s Exact, Supplementary Figure L3). The suppression of somatic mutation is consistent with important functional roles of these elements within tumor cells, highlighting a potential alternative set of targets for examination in cancer.

Common variants associated with human disease and phenotypes

In recent years, GWAS have greatly extended our knowledge of genetic loci associated with human disease risk and other phenotypes. The output of these studies is a series of SNPs (“GWAS SNPs”) correlated with a phenotype, although not necessarily the functional variants. Strikingly, 88% of associated SNPs are either intronic or intergenic75. We examined 4,860 SNP-phenotype associations for 4,492 SNPs curated in the NHGRI GWAS catalogue75. We found that 12% of these SNPs overlap TF-occupied regions whereas 34% overlap DHSs (Figure 10A). Both figures reflect significant enrichments relative to the overall proportions of 1000 Genomes project SNPs (about 6% and 23%, respectively). Even after accounting for biases introduced by selection of SNPs for the standard genotyping arrays, GWAS SNPs show consistently higher overlap with ENCODE annotations (Figure 10A, see Supplementary Information). Furthermore, after partitioning the genome by density of different classes of functional elements, GWAS SNPs were consistently enriched beyond all the genotyping SNPs in function-rich partitions, and depleted in function-poor partitions (see Supplementary Figure M1). GWAS SNPs are particularly enriched in the segmentation classes associated with enhancers and TSSs across several cell types (see Supplementary Figure M2).

Figure 10
Comparison of Genome-wide Association Study-identified Loci with ENCODE Data

Examining the SOM of integrated ENCODE annotations (see above), we found 19 SOM map units showing significant enrichment for GWAS SNPs, including many SOM units previously associated with specific gene functions, such as the immune response regions. Thus, an appreciable proportion of SNPs identified in initial GWAS scans are either functional or lie within the length of an ENCODE annotation (~500 bp on average) and represent plausible candidates for the functional variant. Expanding the set of feasible functional SNPs to those in reasonable linkage disequilibrium, up to 71% of GWAS SNPs have a potential causative SNP overlapping a DNaseI site, and 31% of loci have a candidate SNP that overlaps a binding site occupied by a TF (see also refs 74,76).

The GWAS catalogue provides a rich functional categorization from the precise phenotypes being studied. These phenotypic categorizations are non-randomly associated with ENCODE annotations and there is striking correspondence between the phenotype and the identity of the cell type or TF used in the ENCODE assay (Figure 10B). For example, five SNPs associated with Crohn’s disease overlap GATA2-binding sites (P-value 0.003 by random permutation or 0.001 by an empirical approach comparing to the GWAS-matched SNPs; see Supplementary information), and fourteen are located in DHSs found in immunologically relevant cell types. A notable example is a gene desert on chromosome 5p13.1 containing eight SNPs associated with inflammatory diseases. Several are close to or within DHSs in Th1 and Th2 cells as well as peaks of binding by TFs in HUVECs (Figure 10C). The latter cell line is not immunological, but factor occupancy detected there could be a proxy for binding of a more relevant factor, such as GATA3, in T-cells. Genetic variants in this region also affect expression levels of PTGER477, encoding the prostaglandin receptor EP4. Thus, the ENCODE data reinforce the hypothesis that genetic variants in 5p13.1 modulate the expression of flanking genes, and furthermore provide the specific hypothesis that the variants affect occupancy of a GATA factor in an allele-specific manner, thereby influencing susceptibility to Crohn’s disease.

Non-random association of phenotypes with ENCODE cell types strengthens the argument that at least some of the GWAS lead SNPs are functional or extremely close to functional variants. Each of the associations between a lead SNP and an ENCODE annotation remains a credible hypothesis of a particular functional element class or cell type to explore with future experiments. Supplementary Tables M1, M2 and M3 list all 14,885 pairwise associations across the ENCODE annotations. The accompanying papers have a more detailed examination of common variants with other regulatory information 76.


The unprecedented number of functional elements identified in this study provides a valuable resource to the scientific community as well as significantly enhances our understanding of the human genome. Our analyses have revealed many novel aspects of gene expression and regulation as well as the organization of such information, as illustrated by the accompanying papers (see http://www.encodeproject.org/ENCODE/pubs.html for collected ENCODE publications). However, there are still many specific details, particularly about the mechanistic processes which generate these elements and how and where they function, that require additional experiments to elucidate.

The large spread of coverage, from our highest resolution, most conservative set of bases implicated in GENCODE protein coding gene exons (2.9%) or specific protein DNA binding (8.5%) to the broadest, most general set of marks covering the genome (approximately 80%) -- with many gradations in between -- presents a spectrum of elements with different functional properties discovered by ENCODE. 99% of the known bases in the genome are within 1.7 kbp of any ENCODE element, whereas 95% of bases are within 8 kb of a bound TF motif or DNaseI footprint. Interestingly, even using the most conservative estimates, the fraction of bases likely to be involved in direct gene regulation, even though incomplete, is significantly higher than that ascribed to protein coding exons (1.2%), raising the possibility that more information in the human genome may be important for gene regulation than for biochemical function. Many of the regulatory elements are not constrained across mammalian evolution, which to date has been one of the most reliable indication of an important biochemical event for the organism. Thus, our data provide orthologous indicators for suggesting possible functional elements.

Importantly, for the first time we have sufficient statistical power to assess the impact of negative selection on primate-specific elements, and all ENCODE classes display evidence of negative selection in these unique to primate elements. Furthermore, even with our most conservative estimate of functional elements (8.5% of putative DNA:protein binding regions) and assuming that we have already sampled half of the elements from our TF and cell type diversity, one would estimate that at a minimum 20% (17% from protein binding, and 2.9% protein coding gene exons) of the genome participates in these specific functions, with the likely figure significantly higher.

The broad coverage of ENCODE annotations enhances our understanding of common diseases with a genetic component, rare genetic diseases, and cancer, as shown by our ability to link otherwise anonymous associations to a functional element. ENCODE and similar studies provide a first step towards interpreting the rest of the genome— beyond protein-coding genes—thereby augmenting common disease genetic studies with testable hypotheses. Such information justifies performing whole-genome sequencing (rather than exome only, 1.2% of the genome) on rare diseases and investigating somatic variants in non-coding functional elements, for instance, in cancer. Furthermore since GWAS analyses typically associate disease to SNPs in large regions, comparison to ENCODE non-coding functional elements can help pinpoint putative causal variants in addition to refinement of location by fine-mapping techniques78. Combining ENCODE data with allele-specific information derived from individual genome sequences, provides specific insight on the impact of a genetic variant. Indeed, we believe a significant goal would be to use functional data such as that derived from this project to assign every genomic variant to its possible impact on human phenotypes.

To date, ENCODE has sampled 119 of 1,800 known TFs and general components of the transcriptional machinery on a limited number of cell types and 13 of more than 60 currently known histone or DNA modifications across 147 cell types. DNaseI, FAIRE and extensive RNA assays across subcellular fractionations have been undertaken on many cell types, but overall these data reflect a minor fraction of the potential functional information encoded in the human genome. An important future goal will be to enlarge this dataset to additional factors, modifications and cell types, complementing the other related projects in this area (e.g., Roadmap Epigenomics Project, http://www.roadmapepigenomics.org/ and International Human Epigenome Consortium, http://www.ihec-epigenomes.org/). These projects will constitute foundational resources for human genomics, allowing a deeper interpretation of the organization of gene and regulatory information and the mechanisms of regulation and thereby provide important insights in human health and disease.

A full listing of the Supplementary Figures and Tables is provided in the Supplementary file “ENCODE Supplementary Figures and Tables.docx”. Additional tables are provided as stand alone files as detailed in the index of “ENCODE Supplementary Figures and Tables.docx”. The file “ENCODE Supplementary Info.docx” contains detailed analysis methods and descriptions of code provided, along with descriptions of additional analysis and figures. The supplementary information is accompanied by a Virtual Machine (VM) containing the functioning analysis data and code. Further details of the VM are available from http://encodeproject.org/ENCODE/integrativeAnalysis/VM

Supplementary Material




We thank additional members of our laboratories and institutions who have contributed to the experimental and analytical components of this project. We thank Darryl Leja for assistance with production of the figures. The Consortium is funded by grants from the NHGRI as follows: Production Grants: U54HG004570 (Bernstein); U01HG004695 (Birney); U54HG004563 (Crawford); U54HG004557 (Gingeras); U54HG004555 (Hubbard); U41HG004568 (Kent); U54HG004576 (Myers); U54HG004558 (Snyder); U54HG004592 (Stamatoyannopoulos). Pilot Grants: R01HG003143 (Dekker); RC2HG005591 and R01HG003700 (Giddings); R01HG004456-03 (Ruan); U01HG004571 (Tenenbaum); U01HG004561 (Weng); RC2HG005679 (White). This project was supported in part by American Recovery and Reinvestment Act (ARRA) funds from the NHGRI through grants U54HG004570, U54HG004563, U41HG004568, U54HG004592, R01HG003143, RC2HG005591, R01HG003541, U01HG004561, RC2HG005679 and R01HG003988 (PI: Pennacchio). In addition, work from NHGRI Groups was supported by the Intramural Research Program of the NHGRI (Elnitski, ZIAHG200323; Margulies, ZIAHG200341). Research in the Pennachio lab was performed at Lawrence Berkeley National Laboratory and at the United States Department of Energy Joint Genome Institute, Department of Energy Contract DE-AC02-05CH11231, University of California.


Overall Coordination (Data Analysis Coordination)

Ian Dunham 1, Anshul Kundaje 2,.

Data Production Leads (Data Production)

Shelley F. Aldred 3, Patrick J. Collins 3, Carrie A. Davis 4, Francis Doyle 5, Charles B. Epstein 6, Seth Frietze 7, Jennifer Harrow 8, Rajinder Kaul 9, Jainab Khatun 10, Bryan R. Lajoie 11, Stephen G. Landt 12, Bum-Kyu Lee 13, Florencia Pauli 14, Kate R. Rosenbloom 15, Peter Sabo 16, Alexias Safi 17, Amartya Sanyal 11, Noam Shoresh 6, Jeremy M. Simon 18, Lingyun Song 17, Nathan D. Trinklein 3.

Lead Analysts (Data Analysis)

Robert C. Altshuler 19, Ewan Birney 1, James B. Brown 20, Chao Cheng 21, Sarah Djebali 22, Xianjun Dong 23, Ian Dunham 1, Jason Ernst 19,, Terrence S. Furey 24, Mark Gerstein 21, Belinda Giardine 25, Melissa Greven 23, Ross C. Hardison 25,26, Robert S. Harris 25, Javier Herrero 1, Michael M. Hoffman 16, Sowmya Iyer 27, Manolis Kellis 19, Jainab Khatun 10, Pouya Kheradpour 19, Anshul Kundaje 2,, Timo Lassmann 28, Qunhua Li 20,§, Xinying Lin 23, Georgi K. Marinov 29, Angelika Merkel 22, Ali Mortazavi 30, Stephen C. J. Parker 31, Timothy E. Reddy 14,[perpendicular], Joel Rozowsky 21, Felix Schlesinger 4, Robert E. Thurman 16, Jie Wang 23, Lucas D. Ward 19, Troy W. Whitfield 23, Steven P. Wilder 1, Weisheng Wu 25, Hualin S. Xi 32, Kevin (Yuk-Lap) Yip 21,||, Jiali Zhuang 23.

Writing Group

Bradley E. Bernstein 6,33, Ewan Birney 1, Ian Dunham 1, Eric D. Green 34, Chris Gunter 14, Michael Snyder 12.

NHGRI Project Management (Scientific Management)

Michael J. Pazin 35, Rebecca F. Lowdon 35,[nabla] Laura A.L. Dillon 35, O, Leslie B. Adams 35, Caroline J. Kelly 35, Julia Zhang 35,††, Judith R. Wexler 35,‡‡, Eric D. Green 34, Peter J. Good 35, Elise A. Feingold 35.

Principal Investigators (Steering Committee)

Bradley E. Bernstein 6,33, Ewan Birney 1, Gregory E. Crawford 17,36, Job Dekker 11, Laura Elnitski 37, Peggy J. Farnham 7, Mark Gerstein 21, Morgan C. Giddings 10, Thomas R. Gingeras 4,38, Eric D. Green 34, Roderic Guigó 22,39, Ross C. Hardison 25,26, Timothy J. Hubbard 8, Manolis Kellis 19, W. James Kent 15, Jason D. Lieb 18, Elliott H. Margulies 31,§§, Richard M. Myers 14, Michael Snyder 12, John A. Stamatoyannopoulos 40, Scott A. Tenenbaum 5, Zhiping Weng 23, Kevin P. White 41, Barbara Wold 29,42.

Boise State University Proteomics Group (Data Production and Analysis)

Jainab Khatun 10, Yanbao Yu 43, John Wrobel 10, Brian A. Risk 10, Harsha P. Gunawardena 43, Heather C. Kuiper 43, Christopher W. Maier 43, Ling Xie 43, Xian Chen 43, Morgan C. Giddings 10.

Broad Institute Group (Data Production and Analysis)

Bradley E. Bernstein 6,33, Charles B. Epstein 6, Noam Shoresh 6, Jason Ernst 19,, Pouya Kheradpour 19, Tarjei S. Mikkelsen 6, Shawn Gillespie 33, Alon Goren 6,33, Oren Ram 6,33, Xiaolan Zhang 6, Li Wang 6, Robbyn Issner 6, Michael J. Coyne 6, Timothy Durham 6, Manching Ku 6,33, Thanh Truong 6, Lucas D. Ward 19, Robert C. Altshuler 19, Matthew L. Eaton 19, Manolis Kellis 19.

Cold Spring Harbor, University of Geneva, Center for Genomic Regulation, Barcelona, RIKEN, Sanger Institute, University of Lausanne, Genome Institute of Singapore Group (Data Production and Analysis)

Sarah Djebali 22, Carrie A. Davis 4, Angelika Merkel 22, Alex Dobin 4, Timo Lassmann 28, Ali Mortazavi 30, Andrea Tanzer 22, Julien Lagarde 22, Wei Lin 4, Felix Schlesinger 4, Chenghai Xue 4, Georgi K. Marinov 29, Jainab Khatun 10, Brian A. Williams 29, Chris Zaleski 4, Joel Rozowsky 21, Maik Röder 22, Felix Kokocinski 8, [perpendicular][perpendicular], Rehab F. Abdelhamid 28, Tyler Alioto 22,44, Igor Antoshechkin 29, Michael T. Baer 4, Philippe Batut 4, Ian Bell 45, Kimberly Bell 4, Sudipto Chakrabortty 4, Xian Chen 43, Jacqueline Chrast 46, Joao Curado 22, Thomas Derrien 22, || ||, Jorg Drenkow 4, Erica Dumais 45, Jackie Dumais 45, Radha Duttagupta 45, Megan Fastuca 4, Kata Fejes-Toth 4, [nabla][nabla], Pedro Ferreira 22, Sylvain Foissac 45, Melissa J. Fullwood 47, O O, Hui Gao 45, David Gonzalez 22, Assaf Gordon 4, Harsha P. Gunawardena 43, Cédric Howald 46, Sonali Jha 4, Rory Johnson 22, Philipp Kapranov 45, †††, Brandon King 29, Colin Kingswood 22,44, Guoliang Li 48, Oscar J. Luo 47, Eddie Park 30, Jonathan B. Preall 4, Kimberly Presaud 4, Paolo Ribeca 22,44, Brian A. Risk 10, Daniel Robyr 49, Xiaoan Ruan 47, Michael Sammeth 22,44, Kuljeet Singh Sandhu 47, Lorain Schaeffer 29, Lei-Hoon See 4, Atif Shahab 47, Jorgen Skancke 22, Ana Maria Suzuki 28, Hazuki Takahashi 28, Hagen Tilgner 22, ‡‡‡, Diane Trout 29, Nathalie Walters 46, Huaien Wang 4, John Wrobel 10, Yanbao Yu 43, Yoshihide Hayashizaki 28, Jennifer Harrow 8, Mark Gerstein 21, Timothy J. Hubbard 8, Alexandre Reymond 46, Stylianos E. Antonarakis 49, Gregory J. Hannon 4, Morgan C. Giddings 10, Yijun Ruan 47, Barbara Wold 29,42, Piero Carninci 28, Roderic Guigó 22,39, Thomas R. Gingeras 4,38.

Data Coordination Center at UC Santa Cruz (Production Data Coordination)

Kate R. Rosenbloom 15, Cricket A. Sloan 15, Katrina A. Learned 15, Venkat S. Malladi 15, Matthew C. Wong 15, Galt P. Barber 15, Melissa S. Cline 15, Timothy R. Dreszer 15, Steven G. Heitner 15, Donna Karolchik 15, W. James Kent 15, Vanessa M. Kirkup 15, Laurence R. Meyer 15, Jeffrey C. Long 15, Morgan Maddren 15, Brian J. Raney 15.

Duke University, EBI, University of Texas, Austin, University of North Carolina-Chapel Hill Group (Data Production and Analysis)

Terrence S. Furey 24, Lingyun Song 17, Linda L. Grasfeder 18, Paul G. Giresi 18, Bum-Kyu Lee 13, Anna Battenhouse 13, Nathan C. Sheffield 17, Jeremy M. Simon 18, Kimberly A. Showers 18, Alexias Safi 17, Darin London 17, Akshay A. Bhinge 13, Christopher Shestak 18, Matthew R. Schaner 18, Seul Ki Kim 18, Zhuzhu Z. Zhang 18, Piotr A. Mieczkowski 50, Joanna O. Mieczkowska 18, Zheng Liu 13, Ryan M. McDaniell 13, Yunyun Ni 13, Naim U. Rashid 51, Min Jae Kim 18, Sheera Adar 18, Zhancheng Zhang 24, Tianyuan Wang 17, Deborah Winter 17, Damian Keefe 1, Ewan Birney 1, Vishwanath R. Iyer 13, Jason D. Lieb 18, Gregory E. Crawford 17,36.

Genome Institute of Singapore Group (Data Production and Analysis)

Guoliang Li 48, Kuljeet Singh Sandhu 47, Meizhen Zheng 47, Ping Wang 47, Oscar J. Luo 47, Atif Shahab 47, Melissa J. Fullwood 47, O O, Xiaoan Ruan 47, Yijun Ruan 47.

HudsonAlpha Institute, Caltech, UC Irvine, Stanford Group (Data Production and Analysis)

Richard M. Myers 14, Florencia Pauli 14, Brian A. Williams 29, Jason Gertz 14, Georgi K. Marinov 29, Timothy E. Reddy 14,[perpendicular], Jost Vielmetter 29,42, E. Christopher Partridge 14, Diane Trout 29, Katherine E. Varley 14, Clarke Gasper 29,42, Anita Bansal 14, Shirley Pepke 29,52, Preti Jain 14, Henry Amrhein 29, Kevin M. Bowling 14, Michael Anaya 29,42, Marie K. Cross 14, Brandon King 29, Michael A. Muratet 14, Igor Antoshechkin 29, Kimberly M. Newberry 14, Kenneth McCue 29, Amy S. Nesmith 14, Katherine I. Fisher-Aylor 29,42, Barbara Pusey 14, Gilberto DeSalvo 29,42, Stephanie L. Parker 14, §§§, Sreeram Balasubramanian 29,42, Nicholas S. Davis 14, Sarah K. Meadows 14, Tracy Eggleston 14, Chris Gunter 14, J. Scott Newberry 14, Shawn E. Levy 14, Devin M. Absher 14, Ali Mortazavi 30, Wing H. Wong 53, Barbara Wold 29,42.

Lawrence Berkeley National Laboratory Group (Targeted Experimental Validation)

Matthew J. Blow 54, Axel Visel 54,55, Len A. Pennachio 54,55.

NHGRI Groups (Data Production and Analysis)

Laura Elnitski 37, Elliott H. Margulies 31, §§, Stephen C. J. Parker 31, Hanna M. Petrykowska 37.

Sanger Institute, Washington University, Yale University, Center for Genomic Regulation, Barcelona, UCSC, MIT, University of Lausanne, CNIO Group (Data Production and Analysis)

Alexej Abyzov 21, Bronwen Aken 8, Daniel Barrell 8, Gemma Barson 8, Andrew Berry 8, Alexandra Bignell 8, Veronika Boychenko 8, Giovanni Bussotti 22, Jacqueline Chrast 46, Claire Davidson 8, Thomas Derrien 22, || ||, Gloria Despacio-Reyes 8, Mark Diekhans 15, Iakes Ezkurdia 56, Adam Frankish 8, James Gilbert 8, Jose Manuel Gonzalez 8, Ed Griffiths 8, Rachel Harte 15, David A. Hendrix 19, Cédric Howald 46, Toby Hunt 8, Irwin Jungreis 19, Mike Kay 8, Ekta Khurana 21, Felix Kokocinski 8, [perpendicular][perpendicular], Jing (Jane) Leng 21, Michael F. Lin 19, Jane Loveland 8, Zhi Lu 57, Deepa Manthravadi 8, Marco Mariotti 22, Jonathan Mudge 8, Gaurab Mukherjee 8, Cedric Notredame 22, Baikang Pei 21, Jose Manuel Rodriguez 56, Gary Saunders 8, Andrea Sboner 58, Stephen Searle 8, Cristina Sisu 21, Catherine Snow 8, Charlie Steward 8, Andrea Tanzer 22, Electra Tapanari 8, Michael L. Tress 56, Marijke J. van Baren 59, [perpendicular][perpendicular][perpendicular], Nathalie Walters 46, Stefan Washietl 19, Laurens Wilming 8, Amonida Zadissa 8, Zhengdong Zhang 60, Michael Brent 59, David Haussler 61, Manolis Kellis 19, Alfonso Valencia 56, Mark Gerstein 21, Alexandre Reymond 46, Roderic Guigó 22,39, Jennifer Harrow 8, Timothy J. Hubbard 8.

Stanford-Yale, Harvard, University of Massachusetts Medical School, University of Southern California/UCDavis Group (Data Production and Analysis)

Stephen G. Landt 12, Seth Frietze 7, Alexej Abyzov 21, Nick Addleman 12, Roger P. Alexander 21, Raymond K. Auerbach 21, Suganthi Balasubramanian 21, Keith Bettinger 12, Nitin Bhardwaj 21, Alan P. Boyle 12, Alina R. Cao 62, Philip Cayting 12, Alexandra Charos 63, Yong Cheng 12, Chao Cheng 21, Catharine Eastman 12, Ghia Euskirchen 12, Joseph D. Fleming 64, Fabian Grubert 12, Lukas Habegger 21, Manoj Hariharan 12, Arif Harmanci 21, Sushma Iyengar 65, Victor X. Jin 66, Konrad J. Karczewski 12, Maya Kasowski 12, Phil Lacroute 12, Hugo Lam 12, Nathan Lamarre-Vincent 64, Jing (Jane) Leng 21, Jin Lian 67, Marianne Lindahl-Allen 64, Renqiang Min 21, || || ||, Benoit Miotto 64, Hannah Monahan 63, Zarmik Moqtaderi 64, Xinmeng (Jasmine) Mu 21, Henriette O’Geen 62, Zhengqing Ouyang 12, Dorrelyn Patacsil 12, Baikang Pei 21, Debasish Raha 63, Lucia Ramirez 12, Brian Reed 63, Joel Rozowsky 21, Andrea Sboner 58, Minyi Shi 12, Cristina Sisu 21, Teri Slifer 12, Heather Witt 7, Linfeng Wu 12, Xiaoqin Xu 62, Koon-Kiu Yan 21, Xinqiong Yang 12, Kevin (Yuk-Lap) Yip 21,||, Zhengdong Zhang 60, Kevin Struhl 64, Sherman M. Weissman 67, Mark Gerstein 21, Peggy J. Farnham 7, Michael Snyder 12.

University of Albany SUNY Group (Data Production and Analysis)

Scott A. Tenenbaum 5, Luiz O. Penalva 68, Francis Doyle 5.

University of Chicago, Stanford Group (Data Production and Analysis)

Subhrad ip Karmakar 41, Stephen G. Landt 12, Raj R. Bhanvadia 41, Alina Choudhury 41, Marc Domanus 41, Lijia Ma 41, Jennifer Moran 41, Dorrelyn Patacsil 12, Teri Slifer 12, Alec Victorsen 41, Xinqiong Yang 12, Michael Snyder 12, Kevin P. White 41.

University of Heidelberg Group (Targeted Experimental Validation)

Thomas Auer 69, [nabla][nabla][nabla], Lazaro Centanin 69, Michael Eichenlaub 69, Franziska Gruhl 69, Stephan Heermann 69, Burkhard Hoeckendorf 69, Daigo Inoue 69, Tanja Kellner 69, Stephan Kirchmaier 69, Claudia Mueller 69, Robert Reinhardt 69, Lea Schertel 69, Stephanie Schneider 69, Rebecca Sinn 69, Beate Wittbrodt 69, Jochen Wittbrodt 69.

University of Massachusetts Medical School Bioinformatics Group (Data Production and Analysis)

Zhiping Weng 23, Troy W. Whitfield 23, Jie Wang 23, Patrick J. Collins 3, Shelley F. Aldred 3, Nathan D. Trinklein 3, E. Christopher Partridge 14, Richard M. Myers 14.

University of Massachusetts Medical School Genome Folding Group (Data Production and Analysis)

Job Dekker 11, Gaurav Jain 11, Bryan R. Lajoie 11, Amartya Sanyal 11.

University of Washington, University of Massachusetts Medical Center Group (Data Production and Analysis)

Gayathri Balasundaram 70, Daniel L. Bates 16, Rachel Byron 70, Theresa K. Canfield 16, Morgan J. Diegel 16, Douglas Dunn 16, Abigail K. Ebersol 71, Tristan Frum 71, Kavita Garg 72, Erica Gist 16, R. Scott Hansen 71, Lisa Boatman 71, Eric Haugen 16, Richard Humbert 16, Gaurav Jain 11, Audra K Johnson 16, Ericka M. Johnson 71, Tattyana V. Kutyavin 16, Bryan R. Lajoie 11, Kristen Lee 16, Dimitra Lotakis 71, Matthew T. Maurano 16, Shane J. Neph 16, Fiedencio V. Neri 16, Eric D. Nguyen 71, Hongzhu Qu 16, Alex P. Reynolds 16, Vaughn Roach 16, Eric Rynes 16, Peter Sabo 16, Minerva E. Sanchez 71, Richard S. Sandstrom 16, Amartya Sanyal 11, Anthony O. Shafer 16, Andrew B. Stergachis 16, Sean Thomas 16, Robert E. Thurman 16, Benjamin Vernot 16, Jeff Vierstra 16, Shinny Vong 16, Hao Wang 16, Molly A. Weaver 16, Yongqi Yan 71, Miaohua Zhang 70, Joshua M. Akey 16, Michael Bender 70, Michael O. Dorschner 73, Mark Groudine 70, Michael J. MacCoss 16, Patrick Navas 71, George Stamatoyannopoulos 71, Rajinder Kaul 9, Job Dekker 11, John A. Stamatoyannopoulos 40.

Data Analysis Center (Data Analysis)

Ian Dunham 1, Kathryn Beal 1, Alvis Brazma 74, Paul Flicek 1, Javier Herrero 1, Nathan Johnson 1, Damian Keefe 1, Margus Lukk 74, O O O, Nicholas M. Luscombe 75, Daniel Sobral 1, ††††, Juan M. Vaquerizas 75, Steven P. Wilder 1, Serafim Batzoglou 2, Arend Sidow 76, Nadine Hussami 2, Sofia Kyriazopoulou-Panagiotopoulou 2, Max W. Libbrecht 2, ‡‡‡‡, Marc A. Schaub 2, Anshul Kundaje 2,, Ross C. Hardison 25,26, Webb Miller 25, Belinda Giardine 25, Robert S. Harris 25, Weisheng Wu 25, Peter J. Bickel 20, Balazs Banfai 20, Nathan P. Boley 20, James B. Brown 20, Haiyan Huang 20, Qunhua Li 20,§, Jingyi Jessica Li 20, William Stafford Noble 16,77, Jeffrey A. Bilmes 78, Orion J. Buske 16, Michael M. Hoffman 16, Avinash D. Sahu 16, Peter V. Kharchenko 79, Peter J. Park 79, Dannon Baker80, James Taylor80, Zhiping Weng 23, Sowmya Iyer 27, Xianjun Dong 23, Melissa Greven 23, Xinying Lin 23, Jie Wang 23, Hualin S. Xi 32, Jiali Zhuang 23, Mark Gerstein 21, Roger P. Alexander 21, Suganthi Balasubramanian 21, Chao Cheng 21, Arif Harmanci 21, Lucas Lochovsky 21, Renqiang Min 21, || || ||, Xinmeng (Jasmine) Mu 21, Joel Rozowsky 21, Koon-Kiu Yan 21, Kevin (Yuk-Lap) Yip 21, ||, Ewan Birney 1.


1Vertebrate Genomics Group, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

2Department of Computer Science, Stanford University, 318 Campus Drive, Stanford, CA, USA

3SwitchGear Genomics, 1455 Adams Drive Suite 1317, Menlo Park, CA, USA

4Functional Genomics, Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, NY, USA

5College of Nanoscale Sciences and Engineering, University ay Albany-SUNY, 257 Fuller Road, NFE 4405, Albany, NY, USA

6Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA, USA

7Biochemistry and Molecular Biology, USC/Norris Comprehensive Cancer Center, 1450 Biggy Street, NRT 6503, Los Angeles, CA, USA

8Informatics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

9Department of Medicine, Division of Medical Genetics, University of Washington, 3720 15th Ave NE, Seattle, WA, USA

10College of Arts and Sciences, Boise State University, 1910 University Dr., Boise, ID, USA

11Program in Systems Biology, Program in Gene Function and Expression, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA, USA

12Department of Genetics, Stanford University, 300 Pasteur Dr., M-344, Stanford, CA, USA

13Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, Section of Molecular Genetics and Microbiology, The University of Texas at Austin, 1 University Station A4800, Austin, TX, USA

14HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL, USA

15Center for Biomolecular Science and Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, USA

16Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA, USA

17Institute for Genome Sciences & Policy, Duke University, 101 Science Drive, Durham, NC, USA

18Department of Biology, Carolina Center for Genome Sciences, and Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, 408 Fordham Hall, Chapel Hill, NC, USA

19Computer Science and Artificial Intelligence Laboratory, Broad Institute of MIT and Harvard, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA

20Department of Statistics, University of California, Berkeley, 367 Evans Hall, University of California, Berkeley, Berkeley, CA, USA

21Computational Biology & Bioinformatics Program, Yale University, 266 Whitney Ave, New Haven, CT, USA

22Bioinformatics and Genomics, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88 - 08003, Barcelona, Catalunya, Spain

23Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA, USA

24Department of Genetics, The University of North Carolina at Chapel Hill, 120 Mason Farm Rd, CB#7240, Chapel Hill, NC, USA

25Center for Comparative Genomics and Bioinformatics, The Pennsylvania State University, Wartik Laboratory, University Park, PA, USA

26Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 304 Wartik Laboratory, University Park, PA, USA

27Program in Bioinformatics, Boston University, 24 Cummington St, Boston, MA, USA

28RIKEN Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

29Division of Biology, California Institute of Technology, 156-291200 E. California Blvd, Pasadena, CA, USA

30Developmental and Cell Biology and Center for Complex Biological Systems, University of California Irvine, 2218 Biological Sciences III, Irvine, CA, USA

31Genome Technology Branch, National Human Genome Research Institute, 5625 Fishers Lane, Bethesda, MD, USA

32Department of Biochemistry and Molecular Pharmacology, Bioinformatics Core, University of Massachusetts Medical School, 364 Plantation Street, Worcester, MA, USA

33Howard Hughes Medical Institute and Department of Pathology, Massachusetts General Hospital and Harvard Medical School, 185 Cambridge St CPZN 8400, Boston, MA, USA

34National Human Genome Research Institute, National Institutes of Health, 31 Center Dr., Bldg. 31, Rm. 4B09, Bethesda, MD, USA

35National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, MD, USA

36Department of Pediatrics, Division of Medical Genetics, Duke University School of Medicine, Durham, NC, USA

37National Human Genome Research Institute, National Institutes of Health, 5625 Fishers Lane, Rockville, MD, USA

38Affymetrix, Inc., 3380 Central Expressway, Santa Clara, CA, USA

39Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain

40Department of Genome Sciences, Box 355065, and Department of Medicine, Division of Oncology, Box 358081, University of Washington, Seattle, WA, USA

41Institute for Genomics and Systems Biology, The University of Chicago, 900 E. 57th Street, 10100 KCBD, Chicago, IL, USA

42Beckman Institute, California Institute of Technology, 156-29 1200 E. California Blvd., Pasadena, CA, 91125, US

43Dept. of Biochemistry & Biophysics, University of North Carolina School of Medicine, Campus Box 7260, 120 Mason Farm Rd., #3010 Genetic Medicine Bldg., Chapel Hill, NC, USA

44Centro Nacional de Análisis Genómico (CNAG), C/Baldiri Reixac 4, Torre I, Barcelona, Catalunya 08028, Spain

45Genomics, Affymetrix, Inc., 3380 Central Expressway, Santa Clara, CA, USA

46Center for Integrative Genomics, University of Lausanne, Genopode Building, 1015 Lausanne, Switzerland, Lausanne, Switzerland

47Genome Technology and Biology, Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome,, Singapore 138672, Singapore

48Computational and Systems Biology, Genome Institute of Singapore, 60 Biopolis Street, #02 01, Genome,, Singapore 138672, Singapore

49Department of Genetic Medicine and Development, University of Geneva Medical School, and University Hospitals of Geneva, 1 rue Michel-Servet, Geneva, Switzerland

50Department of Genetics, The University of North Carolina at Chapel Hill, 5078 GMB, Chapel Hill, NC, USA

51Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 408 Fordham Hall, Chapel Hill, NC, USA

52Center for Advanced Computing Research, California Institute of Technology, MC 158-79, 1200 East California Blvd., Pasadena, CA, 91125, USA

53Department Statistics, Stanford University, Sequoia Hall. 390 Serra Mall,, Stanford, CA, USA

54DOE Joint Genome Institute, Walnut Creek, CA, USA

55Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, MS 84-171, Berkeley, CA, USA

56Structural Computational Biology, Spanish National Cancer Research Centre (CNIO), Melchor Fernandez Almagro, 3 - 28029, Madrid, Spain

57School of Life Sciences, Tsinghua University, School of Life Sciences, Tsinghua University, Beijing, 100084, Beijing, China

58Dept. Pathology and Laboratory Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Ave, Box 140, New York, NY, USA

59Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA

60Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Room 353A, Bronxe, NY, USA

61Center for Biomolecular Science and Engineering, Howard Hughes Medical Institute, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, USA

62Genome Center, University of California-Davis, 451 Health Sciences Drive, Davis, CA, USA

63Department of Molecular, Cellular, and Developmental Biology, Yale University, 266 Whitney Ave, New Haven, CT, USA

64Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, MA, USA

65Biochemistry and Molecular Biology, University of Southern California, 1501 San Pablo Street, Los Angeles, CA, USA

66Department of Biomedical Informatics, Ohio State University, 3172C Graves Hall, 333 W Tenth Avenue, Columbus, OH, USA

67Department of Genetics, Yale University, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, USA

68Department of Cellular and Structural Biology, Children’s Cancer Research Institute - UTHSCSA, Mail code 7784-7703 Floyd Curl Dr, San Antonio, TX, USA

69Centre for Organismal Studies (COS) Heidelberg, University of Heidelberg, Im Neuenheimer Feld 230, 69120 Heidelberg, Germany

70Basic Sciences Division, Fred Hutchinson Cancer Research Center, 825 Eastlake Avenue East, Seattle, WA, USA

71Department of Medicine, Division of Medical Genetics, Box 357720, University of Washington, Seattle, WA, USA

72Division of Human Biology, Fred Hutchinson Cancer Research Center, 825 Eastlake Avenue East, Seattle, WA, USA

73Department of Psychiatry and Behavioral Sciences, Box 356560, University of Washington, Seattle, WA, USA

74Microarray Informatics Group, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

75Genomics and Regulatory Systems Group, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

76Department of Pathology, Department of Genetics, Stanford University, 300 Pasteur Drive, Stanford, CA, USA

77Department of Computer Science and Engineering, 185 Stevens Way, Seattle, WA, USA

78Department of Electrical Engineering, University of Washington, 185 Stevens Way, Seattle, WA, USA

79Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA, USA

80Departments of Biology and Mathematics & Computer Science, Emory University, Atlanta, GA, USA

Present Address: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA. Anshul Kundaje

Present Address: UCLA Biological Chemistry Department, Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA, Jonsson Comprehensive Cancer Center; 615 Charles E Young Dr South; Los Angeles, CA 90095, USA. Jason Ernst

§Present Address: Department of Statistics, 514D Wartik Lab, Penn State University, State College, PA, USA. Qunhua Li

[perpendicular]Present Address: Department of Biostatistics & Bioinformatics and the Institute for Genome Sciences & Policy, Duke University School of Medicine, 101 Science Drive, Durham, NC, USA. Timothy E. Reddy

||Present Address: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. Kevin (Yuk-Lap) Yip

[nabla]Present Address: Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, USA. Rebecca F. Lowdon

OPresent Address: Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA. Laura A.L. Dillon

††Present Address: National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. Julia Zhang

‡‡Present Address: University of California, Davis Population Biology Graduate Group, Davis, CA, USA. Judith R. Wexler

§§Present Address: Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Saffron Walden, Essex, CB10 1XL, UK.. Elliott H. Margulies

[perpendicular][perpendicular]Present Address: BlueGnome Ltd., CPC4, Capital Park, Fulbourn, Cambridge, CB21 5XE, UK. Felix Kokocinski

|| ||Present Address: Institut de Génétique et Développement de Rennes, CNRS-UMR6061, Université de Rennes 1, F-35000 Rennes, Brittany, France. Thomas Derrien

[nabla][nabla]Present Address: Caltech, 1200 E. California Blvd., Pasadena, CA 91125, USA. Kata Fejes Toth

O OPresent Address: A*STAR-Duke-NUS Neuroscience Research Partnership, 8 College Road, Singapore 169857. Melissa J. Fullwood

†††Present Address: St. Laurent Institute, One Kendall Square, Cambridge, MA. USA. Philipp K apranov

‡‡‡Present Address: Department of Genetics, Stanford University, Stanford, CA 94305, USA. Hagen Tilgner

§§§Present Address: Biomedical Sciences (BMS) Graduate Program, University of California, San Francisco, 513 Parnassus Ave., HSE-1285, San Francisco, CA 94143-0505, USA. Stephanie L. Parker

[perpendicular][perpendicular][perpendicular]Present Address: Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA. Marijke J. van Baren

|| || ||Present Address: Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Princeton, NJ 08540, USA. Renqiang Min

[nabla][nabla][nabla]Present Address: Neuronal Circuit Development Group, Unité de Génétique et Biologie du Développement, U934/UMR3215, Institut Curie - Centre de Recherche, Pole de Biologie du Développement et Cancer, 26, rue d’Ulm, 75248 PARIS Cedex 05, France. Thomas Auer

O O OPresent Address: Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. Margus Lukk

††††Present Address: Unidade de Bioinformatica, Rua da Quinta Grande, 6, P-2780-156 Oeiras, Portugal. Daniel Sobral

‡‡‡‡Present Address: Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA, USA. Max W. Libbrecht

§§§§Present Address: Center for Bioinformatics and Computational Biology, 3115 Ag/Life Surge Bldg #296, University of Maryland, College Park, MD, USA. Avinash D. Sahu


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