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Index and biological spectrum of human DNase I hypersensitive sites
Open accessPublished version of a preprint
Abstract
DNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA 1–5 and contain genetic variations associated with diseases and phenotypic traits 6–8 . We created high-resolution maps of DHSs from 733 human biosamples encompassing 438 cell and tissue types and states, and integrated these to delineate and numerically index approximately 3.6 million DHSs within the human genome sequence, providing a common coordinate system for regulatory DNA. Here we show that these maps highly resolve the cis -regulatory compartment of the human genome, which encodes unexpectedly diverse cell- and tissue-selective regulatory programs at very high density. These programs can be captured comprehensively by a simple vocabulary that enables the assignment to each DHS of a regulatory barcode that encapsulates its tissue manifestations, and global annotation of protein-coding and non-coding RNA genes in a manner orthogonal to gene expression. Finally, we show that sharply resolved DHSs markedly enhance the genetic association and heritability signals of diseases and traits. Rather than being confined to a small number of distal elements or promoters, we find that genetic signals converge on congruently regulated sets of DHSs that decorate entire gene bodies. Together, our results create a universal, extensible coordinate system and vocabulary for human regulatory DNA marked by DHSs, and provide a new global perspective on the architecture of human gene regulation.
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“However, open chromatin regions are not limited to active enhancers, and also harbour other regions including active gene promoters, regulatory elements in a ‘poised’ chromatin configuration (discussed below), as well as some structural regions such as tissue-invariant binding sites for the architectural protein CCCTC-binding factor (CTCF) [129–132].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [130]
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“A majority of TCs (90%) were supported by FANTOM5 CAGE peaks17 and/or open chromatin regions from ENCODE22 and TCGA7”
From Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes (2023) · cites this paper as [22]
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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,193
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Self-citationLD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
The approximation of one matrix by another of lower rank
Eckart, C., & Young, G. (1936) · Psychometrika · doi:10.1007/bf02288367 · Cited by 4,497
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247
Cited by
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Items 101–200 of 334
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Yang, M. G., Ling, E., Cowley, C. J., Greenberg, M. E., & Vierbuchen, T. (2022) · eLife · doi:10.7554/elife.76500 · Cited by 25
Pividori, M., Lu, S., Li, B., Su, C., Johnson, M. E., Wei, W., Feng, Q., Namjou, B., Kiryluk, K., Kullo, I. J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41057-4 · Cited by 22
Atlas-scale single-cell DNA methylation profiling with sciMETv3
Nichols, R. V., Rylaarsdam, L. E., O’Connell, B. L., Shipony, Z., Iremadze, N., Acharya, S. N., & Adey, A. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100726 · Cited by 23
Modelling and design of transcriptional enhancers
Winter, S. D., Konstantakos, V., & Aerts, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00280-y · Cited by 21
Hypoxia hits APOL1 in the kidney
Grampp, S., Krüger, R., Lauer, V., Uebel, S., Knaup, K. X., Naas, J., Höffken, V., Weide, T., Schiffer, M., Naas, S., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.03.035 · Cited by 21
Capauto, D., Wang, Y., Wu, F., Norton, S., Mariani, J., Inoue, F., Crawford, G. E., Ahituv, N., Abyzov, A. S., & Vaccarino, F. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54302-7 · Cited by 22
Zwifelhofer, N. M., Cai, X., Liao, R., Mao, B., Conn, D. J., Mehta, C., Keleş, S., Xia, Y., & Bresnick, E. H. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009286 · Cited by 21
Cancer-associated chromatin variants uncover the oncogenic role of transposable elements
Grillo, G., & Lupien, M. (2022) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2022.101911 · Cited by 21
Designing synthetic regulatory elements using the generative AI framework DNA-Diffusion
daSilva, L. F., Senan, S., Kribelbauer, J. F., Patel, Z., Louis, L. K., Reddy, A. J., Gabbita, S., Rosen, J. D., Nussbaum, Z., Cordova, C. M. V., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02441-6 · Cited by 36
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Butti, E., Cattaneo, S., Bacigaluppi, M., Cambiaghi, M., Scotti, G. M., Brambilla, E., Ruffini, F., Sferruzza, G., Ripamonti, M., Simeoni, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35341-y · Cited by 22
Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data
Cheng, S., Miao, B., Li, T., Zhao, G., & Zhang, B. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae054 · Cited by 20
Che, H., Jiang, P., Choy, L. Y. L., Cheng, S. H., Peng, W., Chan, R. W. Y., Liu, J., Zhou, Q. X., Lam, W. K., Yu, S. C. Y., et al. (2024) · Genome Research · doi:10.1101/gr.278556.123 · Cited by 18
Liu, Y., Liu, J., Gao, J.-L., Tang, H., & Li, C. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.135590 · Cited by 18
Genetic variants associated with syncope implicate neural and autonomic processes
Aegisdottir, H. M., Þórólfsdóttir, R. B., Sveinbjörnsson, G., Stefánsson, Ó. A., Gunnarsson, B., Tragante, V., Þorleifsson, G., Stefánsdóttir, L., Thorgeirsson, T. E., Ferkingstad, E., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad016 · Cited by 18
Pratt, H., Andrews, G., Shedd, N., Phalke, N. N., Li, T., Pampari, A., Jensen, M., Wen, C. H., Gandal, M. J., Geschwind, D. H., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj4452 · Cited by 21
Machine-guided design of synthetic cell type-specific cis -regulatory elementsPreprint
Gosai, S. J., Castro, R. I., Fuentes, N., Butts, J. C., Kales, S., Noche, R. R., Mouri, K., Sabeti, P. C., Reilly, S. K., & Tewhey, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.08.552077 · Cited by 18
A general genome editing strategy using CRISPR lipid nanoparticle spherical nucleic acids
Han, Z., Huang, C., Luo, T., & Mirkin, C. A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2426094122 · Cited by 17
Mammalian nuclear speckles exhibit stable association with chromatin: a biochemical study
Raina, K., & Rao, B. J. (2022) · Nucleus · doi:10.1080/19491034.2021.2024948 · Cited by 17
The impact of nucleosome structure on CRISPR/Cas9 fidelity
Handelmann, C. R., Tsompana, M., Samudrala, R., & Buck, M. J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad021 · Cited by 17
Salvadores, M., & Supek, F. (2024) · Nature Cancer · doi:10.1038/s43018-023-00707-8 · Cited by 16
Modeling the vertebrate regulatory sequence landscape by UUATAC-seq and deep learning
Han, X., Wu, H., Wang, X., Liu, D., Fu, Y., Yang, L. X., Wang, R., Zhang, P., Wang, J., Ma, L., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.020 · Cited by 16
Sex-heterogeneous SNPs disproportionately influence gene expression and health
Traglia, M., Bout, M., & Weiss, L. A. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010147 · Cited by 17
Alda-Catalinas, C., Ibarra-Soria, X., Flouri, C., Gordillo, J. E., Cousminer, D. L., Hutchinson, A., Sun, B., Pembroke, W. G., Ullrich, S., Krejčí, A., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03176-z · Cited by 18
A conserved NR5A1-responsive enhancer regulates SRY in testis-determination
Houzelstein, D., Eozénou, C., Lagos, C. F., Elzaïat, M., Bignon‐Topalovic, J., González, I., Laville, V., Schlick, L., Wankanit, S., Madon, P. F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47162-2 · Cited by 16
Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns
Wong, D., Tageldein, M. M., Luo, P., Ensminger, E., Bruce, J. P., Oldfield, L. E., Gong, H., Fischer, N. W., Laverty, B., Subasri, V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51529-w · Cited by 20
Cavalli, M., Diamanti, K., Dang, Y., Xing, P., Pan, G., Chen, X., & Wadelius, C. (2021) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2021.0093 · Cited by 16
A versatile toolkit for molecular QTL mapping and meta-analysis at scalePreprint
Quick, C., Guan, L., Li, Z., Li, X., Dey, R., Liu, Y., Scott, L. J., & Lin, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.18.423490 · Cited by 16
Buffering and non-monotonic behavior of gene dosage response curves for human complex traitsPreprint
Milind, N., Smith, C. J., Zhu, H., Gjorgjieva, T., Spence, J. P., & Pritchard, J. K. (2024) · medRxiv · doi:10.1101/2024.11.11.24317065 · Cited by 15
Kosicki, M., Cintrón, D. L., Keukeleire, P., Schubach, M., Page, N. F., Georgakopoulos‐Soares, I., Akiyama, J. A., Plajzer-Frick, I., Novak, C. S., Kato, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60064-1 · Cited by 27
Variant-specific priors clarify colocalisation analysis
Pullin, J. M., & Wallace, C. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011697 · Cited by 18
Multiomics and deep learning dissect regulatory syntax in human development
Liu, B. B., Jessa, S., Kim, S. H., Ng, Y. T., Higashino, S. I., Marinov, G. K., Chen, D. C., Parks, B. E., Li, L., Nguyen, T. C., et al. (2026) · Nature · doi:10.1038/s41586-026-10326-9 · Cited by 36
Lin, X., Chen, F., Zhang, K., Li, J., Jiang, J., & Yu, R. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04984 · Cited by 15
Integrated, Longitudinal Analysis of Cell-free DNA in Uveal Melanoma
Wong, D., Luo, P., Znassi, N., Arteaga, D. P., Gray, D., Danesh, A., Han, M., Stutheit-Zhao, E. Y., Pedersen, S., Prokopec, S. D., et al. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-22-0456 · Cited by 16
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Sinnott-Armstrong, N., Fields, S., Roth, F. P., Starita, L. M., Trapnell, C., Villén, J., Fowler, D. M., & Queitsch, C. (2024) · eLife · doi:10.7554/elife.88231 · Cited by 14
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Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 18
Xiang, G., Giardine, B. M., Mahony, S., Zhang, Y., & Hardison, R. C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab148 · Cited by 14
Autio, M. I., Motakis, E., Perrin, A., Amin, T. B., Tiang, Z., Do, D. V., Wang, J., Tan, J. H. J., Ding, S. S. L., Tan, W. X., et al. (2024) · eLife · doi:10.7554/elife.79592 · Cited by 13
Tissue context determines the penetrance of regulatory DNA variation
Halow, J. M., Byron, R., Hogan, M. S., Ordóñez, R., Groudine, M. T., Bender, M. A., Stamatoyannopoulos, J. A., & Maurano, M. T. (2021) · Nature Communications · doi:10.1038/s41467-021-23139-3 · Cited by 15
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Brandstetter, K., Zülske, T., Ragoczy, T., Hörl, D., Guirao‐Ortiz, M., Steinek, C., Barnes, T., Stumberger, G., Schwach, J., Haugen, E., et al. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.02.009 · Cited by 15
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Higashijima, Y., & Kanki, Y. (2021) · FEBS Journal · doi:10.1111/febs.16089 · Cited by 14
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Tuong, Z. K., Stewart, B. J., Guo, S. A., & Clatworthy, M. R. (2021) · Immunological Reviews · doi:10.1111/imr.13036 · Cited by 13
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad026 · Cited by 13
Fernández, J. D., Navarro‐Payá, D., Santiago, A., Cerda, A., Canan, J., Contreras-Riquelme, S., Moyano, T. C., Landaeta-Sepúlveda, D., Melet, L., Canales, J., et al. (2025) · Plant Communications · doi:10.1016/j.xplc.2025.101499 · Cited by 11
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Wang, M., Li, Q., & Liu, L. (2023) · Biomolecules · doi:10.3390/biom13020304 · Cited by 15
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Trastulla, L., Dolgalev, G. V., Moser, S., Jiménez-Barrón, L. T., Andlauer, T. F. M., von Scheidt, M., Ruderfer, D. M., Ripke, S., McQuillin, A., Stahl, E. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49338-2 · Cited by 12
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Peng, S., Petersen, J. L., Bellone, R. R., Kalbfleisch, T., Kingsley, N. B., Barber, A. M., Cappelletti, E., Giulotto, E., & Finno, C. J. (2021) · Genes · doi:10.3390/genes12111707 · Cited by 10
Dozmorov, M. G., Marshall, M. A., Rashid, N. S., Grible, J. M., Valentine, A. D., Olex, A. L., Murthy, K., Chakraborty, A., Reyna, J., Figueroa, D. S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-32568-7 · Cited by 12
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Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R., Xi, W., Tan, A. S., Galante, J., et al. (2026) · Nature · doi:10.1038/s41586-026-10781-4 · Cited by 161
Self-citationGenome-wide association study on 13 167 individuals identifies regulators of blood CD34+cell levels
Portilla, A. L. D. L., Ekdahl, L., Cafaro, C., Ali, Z., Miharada, N., Þorleifsson, G., Žemaitis, K., Arrizabalaga, A. L., Thodberg, M., Pertesi, M., et al. (2022) · Blood · doi:10.1182/blood.2021013220 · Cited by 11
Bellucci, G., Umeton, R., Bigi, R., Romano, S., Buscarinu, M. C., Reniè, R., Rinaldi, V., Umeton, R. P., Morena, E., Romano, C., et al. (2021) · Scientific Reports · doi:10.1038/s41598-022-11444-w · Cited by 13
Swanson, E. G., Lord, C., Reading, J., Heubeck, A. T., Savage, A. K., Green, R. R., Li, X., Torgerson, T. R., Bumol, T. F., Graybuck, L. T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.04.283887 · Cited by 11
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Bakker, O. B., Claringbould, A., Westra, H.-J., Wiersma, H. H., Boulogne, F., Võsa, U., Symmons, S. M., Jonkers, I. H., Franke, L., & Deelen, P. (2021) · medRxiv · doi:10.1101/2021.10.21.21265342 · Cited by 11
Zaim, S. R., Pebworth, M.-P., McGrath, I., Okada, L. Y., Weiss, M. D. A., Reading, J., Czartoski, J., Torgerson, T. R., McElrath, M. J., Bumol, T. F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50612-6 · Cited by 10
Ho, C., Dippel, M. A., McQuade, M. S., Nguyen, L. P., Mishra, A., Pribitzer, S., Hardy, S. R., Chandok, H., Chardon, F. M., McDiarmid, T. A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02301-3 · Cited by 12
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Hon, C.-C., & Carninci, P. (2020) · Nature · doi:10.1038/d41586-020-02139-1 · Cited by 10
Han, S. K., Muto, Y., Wilson, P. C., Humphreys, B. D., Sampson, M. G., Chakravarti, A., & Lee, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2212810119 · Cited by 10
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Kim, A., Zhang, Z., Legros, C., Lu, Z., de Smith, A. J., Moore, J. E., Durvasula, A., Mancuso, N., & Gazal, S. (2024) · medRxiv · doi:10.1101/2024.05.17.24307556 · Cited by 10
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Liu, B. B., Jessa, S., Kim, S. H., Ng, Y. T., Higashino, S. I., Marinov, G. K., Chen, D. C., Parks, B. E., Li, L., Nguyen, T. C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.30.651381 · Cited by 10
Hennick, K. M., Sui, Y., Karunakaran, D., Nicolella, A. A., Leonard, R., Meyer‐Schuman, R., Berk-Rauch, H., Wang, T., Chakravarti, A., Zoghbi, H. Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.04.674293 · Cited by 13
Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation
Xiang, G., Giardine, B. M., An, L., Sun, C., Keller, C. A., Heuston, E. F., Anderson, S. M., Kirby, M., Bodine, D. M., Zhang, Y., et al. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05223-1 · Cited by 9
Biochemical properties of chromatin domains define genome compartmentalization
Lucini, F., Petrini, C., Salviato, E., Pal, K., Rosti, V., Gorini, F., Santarelli, P., Quadri, R., Lembo, G., Graziano, G., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae454 · Cited by 10
Secchia, S., Beilinson, V., Chen, X., Yang, Z. F., Wayman, J. A., Dhaliwal, J., Jurickova, I., Angerman, E., Denson, L. A., Miraldi, E. R., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115896 · Cited by 14
Tognon, M., Kumbara, A., Betti, A., Ruggeri, L., & Giugno, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf363 · Cited by 9
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Kojima, S., Koyama, S., Ka, M., Saito, Y., Parrish, E. H., Endo, M., Takata, S., Mizukoshi, M., Hikino, K., Takeda, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.25.485726 · Cited by 9
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Zhou, H., Clark, E. L., Guan, D., Lagarrigue, S., Fang, L., Cheng, H., Tuggle, C. K., Kapoor, M., Wang, Y., Giuffra, E., et al. (2024) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-111523-102217 · Cited by 10
Dudek, M. F., Wenz, B. M., Brown, C. D., Voight, B. F., Almasy, L. A., & Grant, S. F. A. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.03.019 · Cited by 8
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Semaan, K., Nawfal, R., Canniff, J., Savignano, H., Seo, J.-H., Siegmund, S., Wu, S. J., Rotow, J. K., Lee, G. M., Chehade, R. E. H., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01031-3 · Cited by 8
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Cao, Z., Huang, Y., Duan, R., Jin, P., Qin, Z. S., & Zhang, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab438 · Cited by 8
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Yi, X., Zheng, Z., Xu, H., Zhou, Y., Huang, D., Wang, J., Feng, X., Zhao, K., Fan, X., Zhang, S., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103468 · Cited by 8
de Langen, P., Hammal, F., Guéret, É., Mouren, J.-C., Spinelli, L., & Ballester, B. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100411 · Cited by 7
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Shahraki, M. F., Farahbod, M., & Libbrecht, M. W. (2024) · Genome Research · doi:10.1101/gr.278343.123 · Cited by 6
Kosicki, M., Cintrón, D. L., Page, N. F., Georgakopoulos‐Soares, I., Akiyama, J. A., Plajzer-Frick, I., Novak, C. S., Kato, M., Hunter, R. D., von Maydell, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.22.590634 · Cited by 8
DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants
Biddie, S. C., Weykopf, G., Hird, E. F., Friman, E. T., & Bickmore, W. A. (2024) · Genome biology · doi:10.1186/s13059-024-03352-1 · Cited by 8
Large-scale discovery of potent, compact and erythroid specific enhancers for gene therapy vectors
Psatha, N., Sova, P., Georgolopoulos, G., Paschoudi, K., Iwata, M., Bloom, J. E., Ulyanova, T., Wang, H., Kirtsou, A., Vasiloudis, N.-I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59235-x · Cited by 9
Self-citationGarinet, S., Semaan, K., Li, J., Zhang, Z., Konda, P., Sadagopan, A., Canniff, J., Phillips, N., Klega, K. S., Pandey, M., et al. (2026) · Journal of Clinical Investigation · doi:10.1172/jci195725 · Cited by 3
Transposable elements shape stemness in normal and leukemic hematopoiesis
Grillo, G., Nadorp, B., Qamra, A., Drylie, B., Mitchell, A., Arlidge, C., Nand, A., Takayama, N., Murison, A., Tonekaboni, S. A. M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02585-z · Cited by 11
Heart Enhancers: Development and Disease Control at a Distance
Yuan, X., Scott, I. C., & Wilson, M. D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.642975 · Cited by 7
An RNA Polymerase III General Transcription Factor Engages in Cell Type-Specific Chromatin Looping
de Llobet, L., Vona, C. D., Morselli, M., Vezzoli, M., Montanini, B., Teichmann, M., de la Luna, S., & Ferrari, R. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042260 · Cited by 7
Cross-ancestry genetic architecture and prediction for cholesterol traits
Momin, M. M., Zhou, X., Hyppönen, E., Benyamin, B., & Lee, S. (2024) · Human Genetics · doi:10.1007/s00439-024-02660-7 · Cited by 6
Benaglio, P., Newsome, J. M., Han, J. Y., Chiou, J., Aylward, A., Corban, S., Okino, M.-L., Kaur, J., Gorkin, D. U., & Gaulton, K. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.03.387894 · Cited by 7
Affinity-optimizing variants within the ZRS enhancer disrupt limb developmentPreprint
Lim, F., Ryan, G. E., Le, S. H., Solvason, J. J., Steffen, P., & Farley, E. (2022) · bioRxiv · doi:10.1101/2022.05.27.493789 · Cited by 7
Xiang, G., He, X., Giardine, B. M., Isaac, K. J., Taylor, D. J., McCoy, R. C., Jansen, C. S., Keller, C. A., Wixom, A. Q., Cockburn, A., et al. (2024) · Genome Research · doi:10.1101/gr.277950.123 · Cited by 8
Unique activities of two overlapping PAX6 retinal enhancers
Uttley, K., Papanastasiou, A. S., Lahne, M., Brisbane, J. M., MacDonald, R. B., Bickmore, W. A., & Bhatia, S. (2023) · Life Science Alliance · doi:10.26508/lsa.202302126 · Cited by 5
Ploidy inference from single-cell data: application to human and mouse cell atlases
Takeuchi, F., & Kato, N. (2024) · Genetics · doi:10.1093/genetics/iyae061 · Cited by 6
Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors
Jin, Y.-T., Tan, Y., Gan, Z.-H., Hao, Y.-D., Wang, T., Lin, H., & Tang, B. (2024) · Methods · doi:10.1016/j.ymeth.2024.06.012 · Cited by 6
A Unified-Field Theory of Genome Organization and Gene Regulation
Negro, G., Semeraro, M., Cook, P. R., & Marenduzzo, D. (2022) · iScience · doi:10.1016/j.isci.2024.111218 · Cited by 8
Bakker, O. B., Claringbould, A., Westra, H.-J., Wiersma, H. H., Boulogne, F., Võsa, U., Urzúa-Traslaviña, C. G., Symmons, S. M., Zidan, M., Sadler, M. C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80670-1 · Cited by 17
Lin, J.-R., Zhao, Y., Jabalameli, M. R., Nguyen, N., Mitra, J., Swillen, A., Vorstman, J. A. S., Chow, E. W. C., van den Bree, M. B. M., Emanuel, B. S., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02009-y · Cited by 6
Semeraro, M., Negro, G., Suma, A., Gonnella, G., & Marenduzzo, D. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129013 · Cited by 6
Varberg, K. M., Dominguez, E. M., Koseva, B. S., Varberg, J. M., McNally, R. P., Moreno‐Irusta, A., Wesley, E. R., Iqbal, K., Cheung, W., Schwendinger-Schreck, C., et al. (2022) · medRxiv · doi:10.1101/2022.05.25.22275520 · Cited by 6
Francis, M., Gorman, B. R., Bigdeli, T. B., Genovese, G., Voloudakis, G., Bendl, J. M., Zeng, B., Venkatesh, S., Chatzinakos, C., Hoffman, G. E., et al. (2024) · medRxiv · doi:10.1101/2024.04.24.24306301 · Cited by 6
Currin, K. W., Perrin, H. J., Pandey, G. K., Alkhawaja, A. A., Vadlamudi, S., Musser, A. E., Etheridge, A. S., Broadaway, K. A., Rosen, J. D., Varshney, A., et al. (2025) · Genome Research · doi:10.1101/gr.279741.124 · Cited by 8
Ma, W., Zheng, W., Qin, S., Wang, C., Lei, B., & Liu, Y. (2025) · GigaScience · doi:10.1093/gigascience/giaf083 · Cited by 6
Contribution of Sex‐Biased Expressed Genes in Osteoarthritis
Wang, C., Shi, M., Li, T., Liu, S., Fang, L., Xu, M., Zhang, B., & Shen, J. (2025) · Smart Medicine · doi:10.1002/smmd.70019 · Cited by 5
He, J., Perera, D., Wen, W., Ping, J., Li, Q., Lyu, L., Chen, Z., Shu, X., Long, J., Cai, Q., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1035 · Cited by 8
Suita, Y., Bright, H., Pu, Y., Toruner, M. D., Idehen, J., Tapinos, N., & Singh, R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012272 · Cited by 5
Alatawneh, R., Salomon, Y., Eshel, R., Orenstein, Y., & Birnbaum, R. Y. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1034604 · Cited by 5
A generic reference defined by consensus peaks for scATAC-seq data analysisPreprint
Meng, Q., Wu, X., Chen, W., Zhao, Y., Li, C., Zheng, W., Li, J., Xi, X., Chen, S., Zhang, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.30.542889 · Cited by 5
Genome-Wide Assessment of Pleiotropy Across >1000 Traits from Global BiobanksPreprint
Levin, M. G., Koyama, S., Woerner, J., Zhang, D. Y., Rodriguez, A., Nandi, T., Truong, B. M. T., Abramowitz, S. A., Gupta, H., Kamineni, H., et al. (2025) · medRxiv · doi:10.1101/2025.04.18.25326074 · Cited by 6
Metadata
- Authors
- Meuleman, W., Muratov, A., Rynes, E., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Teodosiadis, A., Reynolds, A., Haugen, E., Nelson, J., Johnson, A., Frerker, M., Buckley, M., Sandstrom, R., Vierstra, J., Kaul, R., & Stamatoyannopoulos, J.
- Affiliations
- Altius Institute for Biomedical Sciences (US)
- University of Washington (US)
- Corresponding author
- Meuleman, Wouter
- Stamatoyannopoulos, John
- ORCID
- Venue
- Nature
- Published
- 2020-07-29
- Volume
- 584
- Issue
- 7820
- Pages
- 244-251
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7422677
- OpenAlex
- W3046013264
- Cited by
- 331
- References
- 60
- Citations by year
- 2026: 40 · 2025: 59 · 2024: 61 · 2023: 58 · 2022: 56 · 2021: 43 · 2020: 14
- Type
- paratext
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Human Genome Research Institute (U54HG007010, UM1HG009444, R35HG011317)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- DNase I hypersensitive sites, cis-regulatory elements, genetic association signals, heritability
- MeSH terms
- Chromatin, Deoxyribonuclease I, DNA, Gene Expression Regulation, Genes, Humans, Promoter Regions, Genetic, Regulatory Sequences, Nucleic Acid, Genome, Human, Molecular Sequence Annotation
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