Integrative analysis of 111 reference human epigenomes
Open accessAbstract
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression; the results annotate candidate regulatory elements in diverse tissues and cell types, their candidate regulators, and the set of human traits for which they show genetic variant enrichment, providing a resource for interpreting the molecular basis of human disease. The goal of the NIH Roadmap Epigenomics Consortium was to generate a reference collection of human epigenomes for primary cells and tissues. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. The results show that disease and trait-associated genetic variants are enriched in predicted tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits and providing a resource for interpreting the molecular basis of human disease.
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Processing of ChIP-seq and ATAC-seq reads adhered to ENCODE/Roadmap guidelines, with small changes.
“The techniques described for processing the ChIP-seq and ATAC-seq reads are based on ENCODE/Roadmap guidelines with a few modifications (Gjoneska et al., 2015; Landt et al., 2012; Roadmap Epigenomics et al., 2015).”
From Loss of epigenetic information as a cause of mammalian aging (2023) · cites this paper as Roadmap Epigenomics et al., 2015
Online Methods §1.2 describes the Roadmap consortium’s ChIP-seq read-mapping and processing procedures, supporting its use as a reference for ChIP-seq processing. That section covers ChIP-seq and DNase-seq, not ATAC-seq; the paper also cannot establish whether the citing authors followed those procedures with only a few modifications. No page number is available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredGenic enhancers and transcribed regions are hotspots for oxidative DNA damage (EnhG1 0.391, EnhG2 0.392, Tx 0.378, TxWk 0.367).
“Similar to the results above, genic enhancers (average RPKM, EnhG1: 0.391 and EnhG2: 0.392) and transcribed regions (average RPKM, Tx: 0.378 and TxWk: 0.367) are hotspots for oxidative DNA damage (Fig. 2b).”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [49]
The Extended Data passages identify EnhG1 and EnhG2 as enhancer states and Tx and TxWk as chromatin states. They do not report oxidative DNA damage hotspots or the four average RPKM values. Searches for oxidative DNA damage and those values found no supporting passage in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredaSNPs are generally depleted from inactive chromatin states, promoters and transcription start sites, but enriched within transcribed and subsets of enhancer elements.
“aSNPs are generally depleted from inactive chromatin states (states not associated with gene expression as defined by [34]) as well as promoters and transcription start sites, but enriched within transcribed and subsets of enhancer elements (Fig 1; full results are available at S5 Table).”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [34]
The analyses considered variants across multiple cell types and functional chromatin states to account for the strong dependency on the cells' chromatin landscapes when assessing the potential activities of introgressed alleles.
“We specifically analysed all of our variants across multiple cell types and functional chromatin states aiming to account for the strong dependency on the cells’ chromatin landscapes [34] when assessing the potential activities of introgressed alleles.”
From Denisovan introgression has shaped the immune system of present-day Papuans (2022) · cites this paper as [34]
DNase-seq and ChIP-seq from ENCODE and the Epigenomics Roadmap and pseudo-bulk single-cell ATAC-seq from CATlas focus the model towards distal regulatory elements.
“as well as DNase-seq and ChIP–seq from ENCODE and the Epigenomics Roadmap31,99 and pseudo-bulk single-cell ATAC–seq data from CATlas100,101, which focuses the model towards distal regulatory elements.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [99]
MAGMA does not account for tissue-specific regulatory relationships, whereas disease risk SNPs are enriched in regulatory elements of the disease-relevant tissue.
“Second, MAGMA does not take into account tissue-specific regulatory relationships, whereas disease risk SNPs are enriched in regulatory elements of the disease-relevant tissue5,6.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [5]
ChIP-seq experiments involving histone modifications have been used to understand the general role of epigenomic changes on gene expression.
“In addition, ChIP-seq experiments involving histone modifications have been used to understand the general role of these epigenomic changes on gene expression [147, 148].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [147]
With the exception of genetic profile, omic profiles such as transcriptome, epigenome, proteome and metabolome differ across tissues and cell types.
“Except for genetic profile being identical across tissues and cell types, all other omic profiles (e.g. transcriptome, epigenome, proteome and metabolome) vary across tissues and cell types.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
Consortia studies have shown that omic profiles have distinct patterns across tissues and cell types.
“On the contrary, consortia studies have demonstrated distinct patterns of omic profiles across tissues and cell types (Bernstein et al., 2012; Roadmap Epigenomics et al., 2015).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
The NIH Roadmap Epigenomics Consortium generated 111 human reference epigenomes for primary cells and tissues, the largest collection so far.
“The national institute of health Roadmap Epigenomics Consortium generated 111 human reference epigenomes for primary cells and tissues, the largest collection so far (Roadmap Epigenomics et al., 2015).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Roadmap Epigenomics et al., 2015
ENCODE and Roadmap Epigenomics have generated maps of histone modifications, transcription-factor binding, chromatin accessibility, three-dimensional genome structure and other regulatory annotations across hundreds of cell types and tissues.
“For example, the ENCODE and Roadmap Epigenomics projects have generated maps of histone modifications, transcription-factor binding, chromatin accessibility, three-dimensional genome structure and other regulatory annotations across hundreds of cell types and tissues107,108.”
From A brief history of human disease genetics (2020) · cites this paper as [108]
GTEx has associated many noncoding variants with expression quantitative-trait loci.
“the Genotype-Tissue Expression (GTEx) project has associated many noncoding variants with expression quantitative-trait loci (eQTLs)”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [9]
ENCODE and Roadmap Epigenomics identified noncoding regions acting as enhancers and promoters.
“the ENCODE and Roadmap Epigenomics (Roadmap) projects have identified noncoding regions acting as enhancers and promoters (8–10).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [9]
Large-scale annotation projects such as the Roadmap Epigenomics Project, the Blueprint Project, and PsychENCODE aim to generate, and make publicly available, high-quality reference annotation maps across a wide variety of human cell and tissue types.
“This search has been facilitated by large-scale annotation projects such as the Roadmap Epigenomics Project [180], the Blueprint Project [181], and PsychENCODE [182]. These projects aim to generate, and make publicly available, high-quality reference annotation maps across a wide variety of human cell and tissue types.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [180]
Within cell-specific enhancers, DNA sequences provide information about the identities of key lineage-determining transcription factors and the activity states of signaling pathways that control the functions of signal-dependent transcription factors.
“Within cell-specific enhancers, DNA sequences provide information about the identities of key LDTFs and the activity states of signaling pathways that control the functions of SDTFs (31, 33–35).”
From An environment-dependent transcriptional network specifies human microglia identity (2017) · cites this paper as [34]
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Self-citationMolecular Signals of Epigenetic States
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The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
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Distinct epigenomic landscapes of pluripotent and lineage-committed human cells
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Self-citationEpigenomic Analysis of Multilineage Differentiation of Human Embryonic Stem Cells
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Self-citationHigh levels of De Novo methylation and altered chromatin structure at CpG islands in cell lines
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Comprehensive methylome map of lineage commitment from haematopoietic progenitors
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Variation in Homeodomain DNA Binding Revealed by High-Resolution Analysis of Sequence Preferences
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Chromatin marks identify critical cell types for fine mapping complex trait variants
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Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
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Self-citationMethylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease
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Global mapping of protein-DNA interactions in vivo by digital genomic footprinting
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Self-citationCell-of-origin chromatin organization shapes the mutational landscape of cancer
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Self-citationGenome-wide chromatin state transitions associated with developmental and environmental cues
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Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci
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Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants
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Self-citationSystematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments
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Self-citationTranscriptional and Epigenetic Dynamics during Specification of Human Embryonic Stem Cells
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Identifying novel constrained elements by exploiting biased substitution patterns
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Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
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Self-citationTranscription factor binding dynamics during human ES cell differentiation
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Self-citationThe Human Epigenome Browser at Washington University
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Self-citation‘Leveling’ the playing field for analyses of single-base resolution DNA methylomes
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Self-citationAnnotating the human genome with Disease Ontology
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A toolbox for -centroids cluster analysis
Leisch, F. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.10.006 · Cited by 254 · cited 1× · see citing works
“To cluster regulatory (that is, enhancer, promoter or dyadic) regions based on their activity patterns across all reference epigenomes, we expressed each region in terms of a binary vector of length n3s, where n is the number of reference epigenomes (111) and s is the number of chromatin states considered.”
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Self-citationMethylated DNA Immunoprecipitation (MeDIP)
Mohn, F., Weber, M., Schübeler, D., & Roloff, T. C. (2008) · Methods in molecular biology · doi:10.1007/978-1-59745-522-0_5 · Cited by 239 · see citing works
Integrative analysis of haplotype-resolved epigenomes across human tissues
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Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia
Berndt, S. I., Skibola, C. F., Vijai, J., Camp, N. J., Nieters, A., Wang, Z., Cozen, W., Monnereau, A., WANG, S. S., Kelly, R. S., et al. (2013) · Nature Genetics · doi:10.1038/ng.2652 · Cited by 205 · see citing works
Dissecting neural differentiation regulatory networks through epigenetic footprinting
Ziller, M. J., Edri, R., Yaffe, Y., Donaghey, J., Pop, R., Mallard, W., Issner, R., Gifford, C. A., Goren, A., Xing, J. C., et al. (2014) · Nature · doi:10.1038/nature13990 · Cited by 197 · see citing works
Self-citationThe NIH Roadmap Epigenomics Program data Resource
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Self-citationSeumois, G., Chávez, L., Gerasimova, A. V., Lienhard, M., Omran, N., Kalinke, L., Vedanayagam, M., Ganesan, A. P. V., Chawla, A., Djukanović, R., et al. (2014) · Nature Immunology · doi:10.1038/ni.2937 · Cited by 172 · see citing works
Reliable prediction of regulator targets using 12 Drosophila genomesPreprint
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Self-citationStevens, M. W., Cheng, J. B., Li, D., Xie, M., Hong, C., Maire, C. L., Ligon, K. L., Hirst, M., Marra, M. A., Costello, J., et al. (2013) · Genome Research · doi:10.1101/gr.152231.112 · Cited by 156 · see citing works
Self-citationFunctional annotation of colon cancer risk SNPs
Yao, L., Tak, Y. G., Berman, B. P., & Farnham, P. (2014) · Nature Communications · doi:10.1038/ncomms6114 · Cited by 120 · see citing works
Self-citationPredicting the human epigenome from DNA motifs
Whitaker, J. W., Chen, Z., & Wang, W. T. (2014) · Nature Methods · doi:10.1038/nmeth.3065 · Cited by 114 · see citing works
Self-citationIntermediate DNA methylation is a conserved signature of genome regulation
Elliott, G., Hong, C., Xing, X., Zhou, X., Li, D., Coarfa, C., Bell, R. J. A., Maire, C. L., Ligon, K. L., Sigaroudinia, M., et al. (2015) · Nature Communications · doi:10.1038/ncomms7363 · Cited by 104 · see citing works
Self-citationInterplay between chromatin state, regulator binding, and regulatory motifs in six human cell typesPreprint
Ernst, J., & Kellis, M. (2013) · Genome Research · doi:10.1101/gr.144840.112 · Cited by 95 · see citing works
Self-citationEpigenomic annotation of genetic variants using the Roadmap Epigenome Browser
Zhou, X., Li, D., Zhang, B., Lowdon, R. F., Rockweiler, N. B., Sears, R., Madden, P. A. F., Smirnov, I. V., Costello, J., & Wang, T. (2015) · Nature Biotechnology · doi:10.1038/nbt.3158 · Cited by 93 · see citing works
Self-citationGenome‐Scale Mapping of DNase I Hypersensitivity
John, S., Sabo, P. J., Canfield, T. K., Lee, K., Vong, S., Weaver, M., Wang, H., Vierstra, J. D., Reynolds, A. P., Thurman, R. E., et al. (2013) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2127s103 · Cited by 92 · see citing works
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Self-citationTackling the epigenome: challenges and opportunities for collaboration
Satterlee, J. S., Schübeler, D., & Ng, H. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1039 · Cited by 83 · see citing works
Comparison and quantitative verification of mapping algorithms for whole-genome bisulfite sequencing
Kunde‐Ramamoorthy, G., Coarfa, C., Laritsky, E., Kessler, N. J., Harris, R. A., Xu, M., Chen, R., Shen, L., Milosavljevic, A., & Waterland, R. A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1325 · Cited by 80 · see citing works
Self-citationThe mouse Gene Expression Database (GXD): updates and enhancements
Hill, D. P. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh069 · Cited by 79 · see citing works
Epigenetic and transcriptional determinants of the human breast
Gascard, P. D., Bilenky, M., Sigaroudinia, M., Zhao, J., Li, L., Carles, A., Delaney, A. D., Tam, A., Kamoh, B., Cho, S. H., et al. (2015) · Nature Communications · doi:10.1038/ncomms7351 · Cited by 69 · see citing works
Self-citationJAGuaR: Junction Alignments to Genome for RNA-Seq Reads
Butterfield, Y. S. N., Kreitzman, M., Thiessen, N., Corbett, R., Li, Y., Pang, J., Ma, Y. P., Jones, S. J. M., & Birol, İ. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102398 · Cited by 55 · see citing works
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Self-citationMethylated DNA Immunoprecipitation
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Regulatory network decoded from epigenomes of surface ectoderm-derived cell types
Lowdon, R. F., Zhang, B., Bilenky, M., Mauro, T., Li, D., Gascard, P. D., Sigaroudinia, M., Farnham, P., Bastian, B. C., Tlsty, T. D., et al. (2014) · Nature Communications · doi:10.1038/ncomms6442 · Cited by 27 · see citing works
Self-citationA potential role for adiponectin receptor 2 (AdipoR2) in the regulation of alcohol intake
Repunte‐Canonigo, V., Berton, F., Cottone, P., Reifel-Miller, A. E., Roberts, A. J., Morales, M., Francesconi, W. G., & Sanna, P. P. (2010) · Brain Research · doi:10.1016/j.brainres.2010.03.060 · Cited by 21 · see citing works
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deepTools2: a next generation web server for deep-sequencing data analysis
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The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
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Self-citationDissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
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Self-citationFunctional mapping and annotation of genetic associations with FUMA
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10 Years of GWAS Discovery: Biology, Function, and Translation
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Long Noncoding RNAs in Cancer Pathways
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The molecular hallmarks of epigenetic control
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A survey of best practices for RNA-seq data analysis
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Identification of common genetic risk variants for autism spectrum disorder
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Predicting effects of noncoding variants with deep learning–based sequence model
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An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues
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Immunopathology of multiple sclerosis
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Benefits and limitations of genome-wide association studies
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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
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A Phase Separation Model for Transcriptional Control
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The Encyclopedia of DNA elements (ENCODE): data portal update
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A genomic mutational constraint map using variation in 76,156 human genomes
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Universal Patterns of Selection in Cancer and Somatic Tissues
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Genome-wide association study identifies 30 loci associated with bipolar disorder
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The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease
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<i>FTO</i> Obesity Variant Circuitry and Adipocyte Browning in Humans
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Self-citationAssociation analysis identifies 65 new breast cancer risk loci
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Self-citationTranscriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
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Self-citationLandscape of X chromosome inactivation across human tissues
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Self-citationAn environment-dependent transcriptional network specifies human microglia identity
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Transcriptional Addiction in Cancer
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The UK10K project identifies rare variants in health and disease
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Self-citationThe chromatin accessibility landscape of primary human cancers
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“Consistent with published reports (12, 18, 29, 30), the degree of DNA methylation was anticorrelated with chromatin accessibility at regulatory elements, and regions lacking chromatin accessibilityweremore frequentlymethylated (fig.”
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Self-citationTargeting the cancer epigenome for therapy
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Comprehensive functional genomic resource and integrative model for the human brain
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Self-citationLeveraging Polygenic Functional Enrichment to Improve GWAS Power
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The Post-GWAS Era: From Association to Function
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Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy
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Self-citationA Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
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Self-citationFrom genome-wide associations to candidate causal variants by statistical fine-mapping
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Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
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A compendium of chromatin contact maps reveals spatially active regions in the human genome
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The genetic architecture of the human cerebral cortex
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“Heritability enrichment was first estimated in active regulatory elements across tissues and cell types (21, 22).”
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ChIP‐Atlas: a data‐mining suite powered by full integration of public ChIP‐seq data
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The trans-ancestral genomic architecture of glycemic traits
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Self-citationOrganoid single-cell genomic atlas uncovers human-specific features of brain development
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RNA splicing is a primary link between genetic variation and disease
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“PAD: was defined as the presence of 2 or more ICD-9-CM codes or CPT codes as outlined in Klarin et al.15, or having 1 code and 2 or more visits to a vascular surgeon within a 14-month period.”
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Self-citationSystematic mapping of functional enhancer–promoter connections with CRISPR interference
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Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
Self-citationDeterminants of enhancer and promoter activities of regulatory elements
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Structural variation in the 3D genome
Spielmann, M., Lupiáñez, D. G., & Mundlos, S. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0007-0 · Cited by 719 · see citing works
Aragam, K. G., Jiang, T., Goel, A., Kanoni, S., Wolford, B. N., Atri, D. S., Weeks, E. M., Wang, M., Hindy, G., Zhou, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01233-6 · Cited by 721 · see citing works
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- Authors
- Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M. J., Amin, V., Whitaker, J. W., Schultz, M. D., Ward, L. D., Sarkar, A., Quon, G., Sandstrom, R. S., Eaton, M. L., Wu, Y.-C., Pfenning, A. R., Wang, X., Claussnitzer, M., Liu, Y., Coarfa, C., Harris, R. A., Shoresh, N., Epstein, C. B., Gjoneska, E., Leung, D., Xie, W., Hawkins, R. D., Lister, R., Hong, C., Gascard, P., Mungall, A. J., Moore, R., Chuah, E., Tam, A., Canfield, T. K., Hansen, R. S., Kaul, R., Sabo, P. J., Bansal, M. S., Carles, A., Dixon, J. R., Farh, K.-H., Feizi, S., Karlic, R., Kim, A.-R., Kulkarni, A., Li, D., Lowdon, R., Elliott, G., Mercer, T. R., Neph, S. J., Onuchic, V., Polak, P., Rajagopal, N., Ray, P., Sallari, R. C., Siebenthall, K. T., Sinnott-Armstrong, N. A., Stevens, M., Thurman, R. E., Wu, J., Zhang, B., Zhou, X., Beaudet, A. E., Boyer, L. A., De Jager, P. L., Farnham, P. J., Fisher, S. J., Haussler, D., Jones, S. J. M., Li, W., Marra, M. A., McManus, M. T., Sunyaev, S., Thomson, J. A., Tlsty, T. D., Tsai, L.-H., Wang, W., Waterland, R. A., Zhang, M. Q., Chadwick, L. H., Bernstein, B. E., Costello, J. F., Ecker, J. R., Hirst, M., Meissner, A., Milosavljevic, A., Ren, B., Stamatoyannopoulos, J. A., Wang, T., & Kellis, M.
- Affiliations
- Broad Institute (US)
- BC Cancer Agency (CA)
- Baylor College of Medicine (US)
- University of California San Diego (US)
- Salk Institute for Biological Studies (US)
- University of Washington (US)
- University of California, San Francisco (US)
- The University of Texas at Dallas (US)
- Washington University in St. Louis (US)
- The University of Queensland (AU)
- Cold Spring Harbor Laboratory (US)
- Massachusetts Institute of Technology (US)
- University of Southern California (US)
- University of California, Santa Cruz (US)
- University of Wisconsin–Madison (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Kellis, Manolis
- ORCID
- Kundaje, Anshul
- Meuleman, Wouter
- Ernst, Jason
- Zhang, Zhizhuo
- Wang, Jianrong
- Ziller, Michael J.
- Whitaker, John W.
- Ward, Lucas D.
- Sarkar, Abhishek
- Quon, Gerald
- Sandstrom, Richard S.
- Eaton, Matthew L.
- Wu, Yi-Chieh
- Pfenning, Andreas R.
- Wang, Xinchen
- Claussnitzer, Melina
- Liu, Yaping
- Coarfa, Cristian
- Harris, R. Alan
- Epstein, Charles B.
- Gjoneska, Elizabeta
- Leung, Danny
- Xie, Wei
- Hawkins, R. David
- Lister, Ryan
- Gascard, Philippe
- Mungall, Andrew J.
- Tam, Angela
- Kaul, Rajinder
- Sabo, Peter J.
- Bansal, Mukul S.
- Dixon, Jesse R.
- Feizi, Soheil
- Kim, Ah-Ram
- Kulkarni, Ashwinikumar
- Li, Daofeng
- Lowdon, Rebecca
- Elliott, GiNell
- Mercer, Tim R.
- Neph, Shane J.
- Onuchic, Vitor
- Polak, Paz
- Ray, Pradipta
- Sallari, Richard C.
- Wu, Jie
- Zhang, Bo
- Zhou, Xin
- Boyer, Laurie A.
- Farnham, Peggy J.
- Fisher, Susan J.
- Haussler, David
- Jones, Steven J. M.
- Li, Wei
- Marra, Marco A.
- McManus, Michael T.
- Sunyaev, Shamil
- Thomson, James A.
- Tlsty, Thea D.
- Tsai, Li-Huei
- Waterland, Robert A.
- Zhang, Michael Q.
- Chadwick, Lisa H.
- Bernstein, Bradley E.
- Costello, Joseph F.
- Ecker, Joseph R.
- Hirst, Martin
- Meissner, Alexander
- Ren, Bing
- Stamatoyannopoulos, John A.
- Wang, Ting
- Kellis, Manolis
- Venue
- Nature
- Published
- 2015-02-18
- Volume
- 518
- Issue
- 7539
- Pages
- 317-330
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4530010
- OpenAlex
- W2076154138
- Cited by
- 6,368
- References
- 124
- Citations by year
- 2026: 329 · 2025: 429 · 2024: 495 · 2023: 564 · 2022: 589 · 2021: 676 · 2020: 678 · 2019: 644 · 2018: 628 · 2017: 560 · 2016: 517 · 2015: 246 · 2014: 6
- FWCI
- 280.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by-nc-sa)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- eScholarship (California Digital Library) (submittedVersion)
- PMC
- Funders
- National Science Foundation (1254200, CAREER)
- Howard Hughes Medical Institute
- Massachusetts General Hospital
- Washington University in St. Louis
- National Natural Science Foundation of China (91019016)
- Tsinghua University (100084)
- Tsinghua National Laboratory for Information Science and Technology
- National Institutes of Health
- National Institute on Aging (R01AG017917, U01AG046152, R01AG015819, P30AG010161, RF1AG015819, RF1AG036042)
- National Heart, Lung, and Blood Institute (R00HL119617, K99HL119617, F32HL110473)
- National Institute of Mental Health (P50MH096890)
- National Institute on Drug Abuse (U01DA025956, R25DA027995, P01DA008227)
- National Human Genome Research Institute (R01HG007175, R01HG007354, RC1HG005334, R01HG004037, U54HG007990)
- National Institute on Deafness and Other Communication Disorders
- National Institute of General Medical Sciences (T32GM007266, T32GM081739, T32GM007198)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK106829)
- National Institute of Neurological Disorders and Stroke (R01NS078839)
- National Institute of Environmental Health Sciences (U01ES017166, R01ES024992, U01ES017156, ES017166, R01ES024984, U01ES017155, T32ES007032, U01ES017154)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD092419, R24HD000836)
- Common Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- human epigenome, histone modifications, DNA accessibility, DNA methylation, RNA expression, regulatory elements, disease-associated variants, gene expression regulation, cell differentiation
- MeSH terms
- Base Sequence, Cells, Cultured, Chromatin, Chromosomes, Human, DNA, Enhancer Elements, Genetic, Histones, Humans, Organ Specificity, Reference Values, RNA, Genetic Variation, Genome, Human, Cell Lineage, DNA Methylation, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics, Datasets as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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