The DOI matches its Crossref record; 123 of the 124 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Self-citationGene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Self-citationAn integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Self-citationMapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · cited 1×
“Extended data (1-12), supplementary figures (S1-S13), supplementary tables (S1-S6) are available in the online version of the paper.”
BackgroundRNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · cited 2×
“(5) Transcription factor binding sites (TFBS) based on ENCODE ChIP-seq data in the H1-ESC cell-line.”
BackgroundComprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Self-citationHigh-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829
Self-citation
Flagged first, then by citations
1 of the 124 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 1,071
A DNA methylation atlas of normal human cell types
Loyfer, N., Magenheim, J., Peretz, A., Cann, G., Bredno, J., Klochendler, A., Fox-Fisher, I., Shabi-Porat, S., Hecht, M., Pelet, T., et al. (2023) · Nature · doi:10.1038/s41586-022-05580-6 · Cited by 656
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675
Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W. D., Hagenaars, S. P., Ritchie, S. J., Marioni, R. E., Fawns‐Ritchie, C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04362-x · Cited by 686
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 669
A molecular portrait of microsatellite instability across multiple cancers
Cortés‐Ciriano, I., Lee, S., Park, W., Kim, T., & Park, P. J. (2017) · Nature Communications · doi:10.1038/ncomms15180 · Cited by 669
Burr, M. L., Sparbier, C. E., Chan, K. L., Chan, Y.-C., Kersbergen, A. J., Lam, E. Y. N., Azidis-Yates, E., Vassiliadis, D., Bell, C. C., Gilan, O., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.08.008 · Cited by 671
“, 2018), and NT2-D1 from GEO: GSE31755 (ENCOD (D and E) Chromatin state discovery tracks in selected human embryonic stem ChromHMM software integrating H3K27me3, H3K4me3, H3K27ac, and H3K36 (Kundaje et al., 2015).”
BackgroundRNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660
Loss of epigenetic information as a cause of mammalian aging
Yang, J.-H., Hayano, M., Griffin, P. T., Amorim, J. A., Bonkowski, M. S., Apostolides, J. K., Salfati, E. L., Blanchette, M., Munding, E. M., Bhakta, M., et al. (2023) · Cell · doi:10.1016/j.cell.2022.12.027 · Cited by 643
Self-citationSystematic analysis of telomere length and somatic alterations in 31 cancer types
Barthel, F. P., Wei, W., Tang, M., Martinez-Ledesma, E., Hu, X., Amin, S. B., Akdemir, K. C., Seth, S., Song, X., Wang, Q., et al. (2017) · Nature Genetics · doi:10.1038/ng.3781 · Cited by 614
Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior
Yap, E.-L., & Greenberg, M. E. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.013 · Cited by 706
Systematic mapping of functional enhancer–promoter connections with CRISPR interference
Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M. (2016) · Science · doi:10.1126/science.aag2445 · Cited by 643
The Key Role of Epigenetics in Human Disease Prevention and Mitigation
Feinberg, A. P. (2018) · New England Journal of Medicine · doi:10.1056/nejmra1402513 · Cited by 638
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610
“(3) Experimental control data are used as an auxiliary input (PAtCh-CAP for ChIP-nexus data59).”
Self-citationKRAB zinc-finger proteins contribute to the evolution of gene regulatory networks
Imbeault, M., Helleboid, P.-Y., & Trono, D. (2017) · Nature · doi:10.1038/nature21683 · Cited by 623
Ultrastructural Details of Mammalian Chromosome Architecture
Krietenstein, N., Abraham, S., Venev, S. V., Abdennur, N., Gibcus, J., Hsieh, T.-H. S., Parsi, K. M., Yang, L., Maehr, R., Mirny, L. A., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.03.003 · Cited by 622
Towards a complete map of the human long non-coding RNA transcriptome
Uszczyńska-Ratajczak, B., Lagarde, J., Frankish, A., Guigó, R., & Johnson, R. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0017-y · Cited by 637
Fine-mapping inflammatory bowel disease loci to single-variant resolution
International Inflammatory Bowel Disease Genetics Consortium, Huang, H., Fang, M., Jostins, L., Umićević Mirkov, M., Boucher, G., Anderson, C. A., Andersen, V., Cleynen, I., Cortes, A., et al. (2017) · Nature · doi:10.1038/nature22969 · Cited by 611
Self-citationA spectral approach integrating functional genomic annotations for coding and noncoding variants
Ionita‐Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. (2016) · Nature Genetics · doi:10.1038/ng.3477 · Cited by 602
Analyses of non-coding somatic drivers in 2,658 cancer whole genomes
Rheinbay, E., Nielsen, M. M., Abascal, F., Wala, J. A., Shapira, O., Tiao, G., Hornshøj, H., Hess, J. M., Juul, R. I., Lin, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1965-x · Cited by 611
Self-citationStudy, T. L. C., Day, F. R., The InterAct Consortium, kConFab/AOCS Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTICAL consortium, Thompson, D. J., Helgason, H., Chasman, D. I., et al. (2017) · Nature Genetics · doi:10.1038/ng.3841 · Cited by 587
Self-citationCano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 613
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586
Sexual-dimorphism in human immune system aging
Márquez, E. J., Chung, C.-H., Marches, R., Rossi, R. J., Nehar-Belaid, D., Eroglu, A., Mellert, D. J., Kuchel, G. A., Banchereau, J., & Ucar, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14396-9 · Cited by 584
Chromosome conformation elucidates regulatory relationships in developing human brain
Won, H., de la Torre-Ubieta, L., Stein, J. L., Parikshak, N. N., Huang, J., Opland, C. K., Gandal, M. J., Sutton, G. J., Hormozdiari, F., Lu, D., et al. (2016) · Nature · doi:10.1038/nature19847 · Cited by 584
Self-citationSystematic identification of genetic influences on methylation across the human life course
Gaunt, T. R., Shihab, H. A., Hemani, G., Min, J. L., Woodward, G., Lyttleton, O., Zheng, J., Duggirala, A., McArdle, W. L., Ho, K., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0926-z · Cited by 598
Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L.-H., & Kellis, M. (2015) · Nature · doi:10.1038/nature14252 · Cited by 576
Self-citationSomatic mutation in single human neurons tracks developmental and transcriptional history
Lodato, M. A., Woodworth, M. B., Lee, S., Evrony, G. D., Mehta, B. K., Karger, A., Lee, S., Chittenden, T. W., D’Gama, A. M., Cai, X., et al. (2015) · Science · doi:10.1126/science.aab1785 · Cited by 570
Johann, P. D., Erkek, S., Zapatka, M., Kerl, K., Buchhalter, I., Hovestadt, V., Jones, D., Sturm, D., Hermann, C., Segura‐Wang, M., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.02.001 · Cited by 586
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537
The emerging role of cell-free DNA as a molecular marker for cancer management
Bronkhorst, A. J., Ungerer, V., & Holdenrieder, S. (2019) · Biomolecular Detection and Quantification · doi:10.1016/j.bdq.2019.100087 · Cited by 566
“For example, methylation signatures differ among tissues and cell types [177,178], and recent stu-”
BackgroundCell-of-origin chromatin organization shapes the mutational landscape of cancer
Polak, P., Karlić, R., Koren, A., Thurman, R., Sandstrom, R., Lawrence, M. S., Reynolds, A., Rynes, E., Vlahoviček, K., Stamatoyannopoulos, J. A., et al. (2015) · Nature · doi:10.1038/nature14221 · Cited by 557
Self-citationDémenais, F., Margaritte‐Jeannin, P., Barnes, K. C., Cookson, W., Altmüller, J., Ang, W., Barr, R. G., Beaty, T. H., Becker, A. B., Beilby, J. P., et al. (2017) · Nature Genetics · doi:10.1038/s41588-017-0014-7 · Cited by 547
ENCODE data at the ENCODE portal
Sloan, C. A., Chan, E. T., Davidson, J. M., Malladi, V. S., Strattan, J. S., Hitz, B. C., Gabdank, I., Narayanan, A. K., Ho, M., Lee, B. T., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1160 · Cited by 549
Teschendorff, A. E., Breeze, C. E., Zheng, S., & Beck, S. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1511-5 · Cited by 555
The UCSC Genome Browser database: 2021 update
Gonzalez, J. N., Zweig, A. S., Speir, M. L., Schmelter, D., Rosenbloom, K. R., Raney, B. J., Powell, C. C., Nassar, L. R., Maulding, N. D., Lee, C. M., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1070 · Cited by 545
“cCREs are a subset of representative DNase hypersensitive sites across ENCODE and Roadmap Epigenomics (26) samples that are supported by either H3K4me3 and H3K27ac histone modifications or CTCF-binding data.”
BackgroundSelf-citationDiscovery of common and rare genetic risk variants for colorectal cancer
Huyghe, J. R., Bien, S. A., Harrison, T. A., Kang, H. M., Chen, S., Schmit, S. L., Conti, D. V., Qu, C., Jeon, J., Edlund, C. K., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0286-6 · Cited by 538
Self-citationNOTCH1 mediates a switch between two distinct secretomes during senescence
Hoare, M., Ito, Y., Kang, T.-W., Weekes, M. P., Matheson, N. J., Patten, D. A., Shetty, S., Parry, A. J., Menon, S., Salama, R., et al. (2016) · Nature Cell Biology · doi:10.1038/ncb3397 · Cited by 522
The impact of cellular metabolism on chromatin dynamics and epigenetics
Reid, M. A., Dai, Z., & Locasale, J. W. (2017) · Nature Cell Biology · doi:10.1038/ncb3629 · Cited by 515
Shrine, N. R. G., Guyatt, A. L., Erzurumluoglu, A. M., Jackson, V. E., Hobbs, B. D., Melbourne, C., Batini, C., Fawcett, K. A., Song, K. S., Sakornsakolpat, P., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0321-7 · Cited by 521
Transcription factors and 3D genome conformation in cell-fate decisions
Stadhouders, R., Filion, G. J. P., & Graf, T. (2019) · Nature · doi:10.1038/s41586-019-1182-7 · Cited by 530
A single-cell atlas of chromatin accessibility in the human genome
Zhang, K., Hocker, J. D., Miller, M., Hou, X., Chiou, J., Poirion, O. B., Qiu, Y., Li, Y. E., Gaulton, K. J., Wang, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.024 · Cited by 502
Self-citationSchwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532
Somatic mutation landscapes at single-molecule resolution
Abascal, F., Harvey, L. M. R., Mitchell, E., Lawson, A. R. J., Lensing, S. V., Ellis, P., Russell, A. J. C., Alcantara, R. E., Baez-Ortega, A., Wang, Y., et al. (2021) · Nature · doi:10.1038/s41586-021-03477-4 · Cited by 546
Molecular control of activation and priming in macrophages
Glass, C. K., & Natoli, G. (2015) · Nature Immunology · doi:10.1038/ni.3306 · Cited by 538
Guo, S., Diep, D. H., Plongthongkum, N., Fung, H.-L., Zhang, K., & Zhang, K. (2017) · Nature Genetics · doi:10.1038/ng.3805 · Cited by 528
Accurate and efficient detection of gene fusions from RNA sequencing data
Uhrig, S., Ellermann, J., Walther, T., Burkhardt, P., Fröhlich, M., Hutter, B., Toprak, U. H., Neumann, O., Stenzinger, A., Scholl, C., et al. (2021) · Genome Research · doi:10.1101/gr.257246.119 · Cited by 526
Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex
Jaffe, A. E., Gao, Y., Deep-Soboslay, A., Tao, R., Hyde, T. M., Weinberger, D. R., & Kleinman, J. E. (2016) · Nature Neuroscience · doi:10.1038/nn.4181 · Cited by 511
The International League Against Epilepsy Consortium on Complex Epilepsies, Abou-Khalil, B., Auce, P., Avbersek, A., Bahlo, M., Balding, D. J., Bast, T., Baum, L., Becker, A. J., Becker, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07524-z · Cited by 482
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00923-x · Cited by 506
Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression
Soldner, F., Stelzer, Y., Shivalila, C., Abraham, B. J., Latourelle, J. C., Barrasa, M. I., Goldmann, J. E., Myers, R. H., Young, R. A., & Jaenisch, R. (2016) · Nature · doi:10.1038/nature17939 · Cited by 507
Clinical epigenetics: seizing opportunities for translation
Berdasco, M., & Esteller, M. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0074-2 · Cited by 516
Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452
AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection
Vierbuchen, T., Ling, E., Cowley, C. J., Couch, C. H., Wang, X., Harmin, D. A., Roberts, C. W. M., & Greenberg, M. E. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.11.026 · Cited by 455
Self-citationGenomic analysis identifies new drivers and progression pathways in skin basal cell carcinoma
Bonilla, X., Parmentier, L., King, B., Bezrukov, F., Kaya, G., Zoete, V., Seplyarskiy, V. B., Sharpe, H. J., McKee, T., Letourneau, A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3525 · Cited by 491
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., Taskesen, E., Hammerschlag, A. R., Okbay, A., Zabaneh, D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3869 · Cited by 490
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430
Kranzler, H. R., Zhou, H., Kember, R. L., Vickers‐Smith, R., Justice, A. C., Damrauer, S. M., Tsao, P. S., Klarin, D. M., Baras, A., Reid, J. G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09480-8 · Cited by 482
DNA methylation and DNA methyltransferases
Edwards, J. R., Yarychkivska, O., Boulard, M., & Bestor, T. H. (2017) · Epigenetics & Chromatin · doi:10.1186/s13072-017-0130-8 · Cited by 521
Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study
Wu, Y., Lin, Z., Li, C., Lin, X. J., Shan, S., Guo, B., Zheng, M.-H., Li, F., Yuan, L., & Li, Z. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01333-7 · Cited by 549
“National Institutes of Health (NIH) Roadmap Epigenomics Consortium published 111 human reference epigenomes.(51) In less than 100 years, the concept of epigenetics has developed rapidly.”
BackgroundSelf-citationDisease variants alter transcription factor levels and methylation of their binding sites
Bonder, M. J., Luijk, R., Zhernakova, D. V., Moed, M., Deelen, P., Vermaat, M., van Iterson, M., van Dijk, F., van Galen, M., Bot, J. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3721 · Cited by 462
Converging pathways in neurodegeneration, from genetics to mechanisms
Gan, L., Cookson, M. R., Petrucelli, L., & Spada, A. R. L. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0237-7 · Cited by 500
One‐carbon metabolism and epigenetics: understanding the specificity
Mentch, S. J., & Locasale, J. W. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12956 · Cited by 510
Cancer epigenetics: Moving forward
Nebbioso, A., Tambaro, F. P., Dell’Aversana, C., & Altucci, L. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007362 · Cited by 500
Tsoi, L. C., Rodriguez, E., Degenhardt, F., Baurecht, H., Wehkamp, U., Volks, N., Szymczak, S., Swindell, W. R., Sarkar, M. K., Raja, K., et al. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.12.018 · Cited by 469
Genetic adaptation and Neandertal admixture shaped the immune system of human populations
Quach, H., Rotival, M., Pothlichet, J., Loh, Y.-H. E., Dannemann, M., Zidane, N., Laval, G., Patin, E., Harmant, C., Lopez, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.024 · Cited by 475
Gupta, R. M., Hadaya, J., Trehan, A., Zekavat, S. M., Roselli, C., Klarin, D., Emdin, C. A., Hilvering, C. R. E., Bianchi, V., Mueller, C., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.049 · Cited by 450
Self-citationSun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459
Role of non-coding sequence variants in cancer
Khurana, E., Fu, Y., Chakravarty, D., Demichelis, F., Rubin, M. A., & Gerstein, M. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.17 · Cited by 459
High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer
Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, F., Frenoy, O., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0424-9 · Cited by 466
Kemp, J. P., Morris, J. A., Medina‐Gómez, C., Forgetta, V., Warrington, N. M., Youlten, S. E., Zheng, J., Gregson, C. L., Grundberg, E., Trajanoska, K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3949 · Cited by 470
Hammal, F., de Langen, P., Bergon, A., Lopez, F., & Ballester, B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab996 · Cited by 458
“The genomic architecture of the regulatory space has started to unfold thanks to large functional genomic consortia (ENCODE (7,8), Roadmap (9)) but more remains to be discovered.”
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 461
Genome-wide association study identifies 112 new loci for body mass index in the Japanese population
Akiyama, M., Okada, Y., Kanai, M., Takahashi, A., Momozawa, Y., Ikeda, M., Iwata, N., Ikegawa, S., Hirata, M., Matsuda, K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3951 · Cited by 455
The IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447
Hannon, E., Lunnon, K., Schalkwyk, L., & Mill, J. (2015) · Epigenetics · doi:10.1080/15592294.2015.1100786 · Cited by 455
Editing the epigenome: technologies for programmable transcription and epigenetic modulation
Thakore, P. I., Black, J. B., Hilton, I. B., & Gersbach, C. A. (2016) · Nature Methods · doi:10.1038/nmeth.3733 · Cited by 451
Identification of type 2 diabetes loci in 433,540 East Asian individuals
Spracklen, C. N., Horikoshi, M., Kim, Y. J., Lin, K., Bragg, F., Moon, S., Ken, S., Tam, C. H. T., Tabara, Y., Kwak, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2263-3 · Cited by 446
Self-citationDeciphering osteoarthritis genetics across 826,690 individuals from 9 populations
Boer, C. G., Hatzikotoulas, K., Southam, L., Stefánsdóttir, L., Zhang, Y., de Almeida, R. C., Wu, T. T., Zheng, J., Hartley, A. E., Teder‐Laving, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.038 · Cited by 453
Genetic Insights Into Biological Mechanisms Governing Human Ovarian Ageing
Ruth, K. S., Day, F. R., Hussain, J., Martínez-Marchal, A., Aiken, C., Azad, A., Thompson, D. J., Knoblochová, L., Abe, H., Tarry‐Adkins, J. L., et al. (2021) · Obstetrical & Gynecological Survey · doi:10.1038/s41586-021-03779-7 · Cited by 442
Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia
Li, Z., Chen, J., Yu, H., He, L., Xu, Y., Zhang, D., Yi, Q., Li, C., Li, X., Shen, J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3973 · Cited by 442
Self-citationGenetics of Inflammatory Bowel Diseases
McGovern, D. P. B., Kugathasan, S., & Cho, J. H. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.08.001 · Cited by 446
Qi, T., Wu, Y., Zeng, J., Zhang, F., Xue, A., Jiang, L., Zhu, Z., Kemper, K., Yengo, L., Zheng, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04558-1 · Cited by 427
Self-citationA review on machine learning principles for multi-view biological data integration
Li, Y., Wu, F., & Ngom, A. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw113 · Cited by 432
Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer
ABCTB Investigators, Milne, R. L., EMBRACE, Collaborators, G. S., HEBON, kConFab/AOCS Investigators, Collaborators, N., Kuchenbaecker, K. B., Michailidou, K., Beesley, J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3785 · Cited by 415
Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control
Hnisz, D., Day, D. S., & Young, R. A. (2016) · Cell · doi:10.1016/j.cell.2016.10.024 · Cited by 420
Ehret, G., Ferreira, T. C., Chasman, D. I., Jackson, A., Schmidt, E. M., Johnson, T., Þorleifsson, G., Luan, J., Donnelly, L. A., Kanoni, S., et al. (2016) · Nature Genetics · doi:10.1038/ng.3667 · Cited by 417
Self-citationExpression Atlas: gene and protein expression across multiple studies and organisms
Papatheodorou, I., Fonseca, N. A., Keays, M., Tang, A., Barrera, E., Bażant, W., Burke, M. L., Füllgrabe, A., Fuentes, A. M.-P., George, N., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1158 · Cited by 431
Pioneer Transcription Factors Initiating Gene Network Changes
Zaret, K. S. (2020) · Annual Review of Genetics · doi:10.1146/annurev-genet-030220-015007 · Cited by 409
Regulatory genomic circuitry of human disease loci by integrative epigenomics
Boix, C. A., James, B. T., Park, Y., Meuleman, W. R. A., & Kellis, M. (2021) · Nature · doi:10.1038/s41586-020-03145-z · Cited by 387
Self-citationFunctionally informed fine-mapping and polygenic localization of complex trait heritability
Weissbrod, O., Hormozdiari, F., Benner, C., Cui, R., Ulirsch, J., Gazal, S., Schoech, A. P., van de Geijn, B., Reshef, Y., Márquez-Luna, C., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00735-5 · Cited by 364
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0345-7 · Cited by 407
Self-citationMulti-Omics Profiling for Health
Babu, M., & Snyder, M. P. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100561 · Cited by 434
Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity
Harley, J. B., Chen, X., Pujato, M. A., Miller, D., Maddox, A., Forney, C. R., Magnusen, A. F., Lynch, A., Chetal, K., Yukawa, M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0102-3 · Cited by 415
Weischenfeldt, J., Dubash, T., Drainas, A. P., Mardin, B. R., Chen, Y., Stütz, A. M., Waszak, S. M., Bosco, G., Halvorsen, A. R., Raeder, B., et al. (2017) · Nature Genetics · doi:10.1038/ng.3722 · Cited by 404
A human cell atlas of fetal chromatin accessibility
Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., et al. (2020) · Science · doi:10.1126/science.aba7612 · Cited by 383
Clinical relevance of blood-based ctDNA analysis: mutation detection and beyond
Keller, L., Belloum, Y., Wikman, H., & Pantel, K. (2020) · British Journal of Cancer · doi:10.1038/s41416-020-01047-5 · Cited by 416
Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels
Study, G. C. K. D., Tin, A., Study, L. C., Program, V. A. M. V., Marten, J., Kuhns, V. L. H., Li, Y., Wuttke, M., Kirsten, H., Sieber, K. B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0504-x · Cited by 398
Genomic basis for RNA alterations in cancer
PCAWG Transcriptome Core Group, Calabrese, C., Davidson, N. R., Demircioğlu, D., Fonseca, N. A., He, Y., Kahles, A., Lehmann, K.-V., Liu, F., Shiraishi, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-1970-0 · Cited by 411
Metadata
- 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
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
