The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
ChromHMM: automating chromatin-state discovery and characterization
Open accessAbstract
Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease-association studies1-5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets, and visualizing the resulting genome-wide maps of chromatin state annotations.
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.
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
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
Mapping and analysis of chromatin state dynamics in nine human cell types
Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011) · Nature · doi:10.1038/nature09906 · Cited by 2,898
Self-citation
Cited by
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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-citationFunctional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277
A comparative encyclopedia of DNA elements in the mouse genome
Yue, F., Cheng, Y., Breschi, A., Vierstra, J., Wu, W., Ryba, T., Sandstrom, R., Ma, Z., Davis, C., Pope, B. D., et al. (2014) · Nature · doi:10.1038/nature13992 · Cited by 1,781
Self-citationSavage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0152-6 · Cited by 1,267
The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease
Astle, W. J., Elding, H., Jiang, T., Allen, D., Ruklisa, D., Mann, A. L., Mead, D., Bouman, H., Riveros-Mckay, F., Kostadima, M. A., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.042 · Cited by 1,224
Transcriptional enhancers: from properties to genome-wide predictions
Shlyueva, D., Stampfel, G., & Stark, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3682 · Cited by 1,356
Cunningham, F., Amode, M. R., Barrell, D., Beal, K., Billis, K., Brent, S., Carvalho-Silva, D., Clapham, P., Coates, G., Fitzgerald, S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1010 · Cited by 1,115
Javierre, B. M., Burren, O. S., Wilder, S. P., Kreuzhuber, R., Hill, S. M., Sewitz, S., Cairns, J., Wingett, S. W., Várnai, C., Thiecke, M. J., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.037 · Cited by 1,046
Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956
Self-citationAn atlas of human long non-coding RNAs with accurate 5′ ends
Hon, C.-C., Ramilowski, J. A., Harshbarger, J., Bertin, N., Rackham, O. J. L., Gough, J., Denisenko, E., Schmeier, S., Poulsen, T. M., Severin, J., et al. (2017) · Nature · doi:10.1038/nature21374 · Cited by 1,007
Common genetic variants influence human subcortical brain structures
Initiative, T. A. D. N., Hibar, D. P., The CHARGE Consortium, EPIGEN, IMAGEN, SYS, Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., et al. (2015) · Nature · doi:10.1038/nature14101 · Cited by 858
Parker, S. C. J., Stitzel, M. L., Taylor, D. L., Orozco, J. M., Erdos, M. R., Akiyama, J. A., van Bueren, K. L., Chines, P. S., Narisu, N., Program, N. C. S., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317023110 · Cited by 665
Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos
Liu, X., Wang, C., Liu, W., Li, J., Li, C., Kou, X., Chen, J., Zhao, Y., Gao, H., Wang, H., et al. (2016) · Nature · doi:10.1038/nature19362 · Cited by 746
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
Determinants of enhancer and promoter activities of regulatory elements
Andersson, R., & Sandelin, A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0173-8 · Cited by 713
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
Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A., & Lis, J. T. (2014) · Nature Genetics · doi:10.1038/ng.3142 · Cited by 693
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
Resolving the 3D Landscape of Transcription-Linked Mammalian Chromatin Folding
Hsieh, T.-H. S., Cattoglio, C., Slobodyanyuk, E., Hansen, A. S., Rando, O. J., Tjian, R., & Darzacq, X. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.03.002 · Cited by 661
RNA 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
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
Cooperative Binding of Transcription Factors Orchestrates Reprogramming
Chronis, C., Fiziev, P., Papp, B., Butz, S., Bonora, G., Sabri, S., Ernst, J., & Plath, K. (2017) · Cell · doi:10.1016/j.cell.2016.12.016 · Cited by 555
Self-citationHierarchical Mechanisms for Direct Reprogramming of Fibroblasts to Neurons
Wapinski, O. L., Vierbuchen, T., Qu, K., Lee, Q. Y., Chanda, S., Fuentes, D. R., Giresi, P. G., Ng, Y. H., Marro, S., Neff, N. F., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.028 · Cited by 627
Cano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 613
Udler, M. S., Kim, J., von Grotthuss, M., Bonàs-Guarch, S., Cole, J. B., Chiou, J., ISGC, C. D. A. O. B. O. M. A. T., Boehnke, M., Laakso, M., Atzmon, G., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002654 · Cited by 542
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-citationTau promotes neurodegeneration through global chromatin relaxation
Frost, B., Hemberg, M., Lewis, J., & Feany, M. B. (2014) · Nature Neuroscience · doi:10.1038/nn.3639 · Cited by 495
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
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T., & Flicek, P. R. (2015) · Genome Biology · doi:10.1186/s13059-015-0621-5 · Cited by 429
Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490
Abundant contribution of short tandem repeats to gene expression variation in humans
Gymrek, M., Willems, T., Guilmatre, A., Zeng, H., Markus, B., Georgiev, S., Daly, M. J., Price, A. L., Pritchard, J. K., Sharp, A. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3461 · Cited by 410
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
Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452
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
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data
Cairns, J., Freire-Pritchett, P., Wingett, S. W., Várnai, C., Dimond, A., Plagnol, V., Zerbino, D., Schoenfelder, S., Javierre, B.-M., Osborne, C., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0992-2 · Cited by 417
Nonlinear control of transcription through enhancer–promoter interactions
Zuin, J., Roth, G., Zhan, Y., Cramard, J., Redolfi, J., Piskadlo, E., Mach, P., Kryzhanovska, M., Tihanyi, G., Kohler, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04570-y · Cited by 415
Hsieh, T.-H. S., Cattoglio, C., Slobodyanyuk, E., Hansen, A. S., Darzacq, X., & Tjian, R. (2022) · Nature Genetics · doi:10.1038/s41588-022-01223-8 · Cited by 406
Towards a comprehensive catalogue of validated and target-linked human enhancers
Gasperini, M., Tome, J. M., & Shendure, J. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0209-0 · Cited by 396
Extensive Variation in Chromatin States Across Humans
Kasowski, M., Kyriazopoulou-Panagiotopoulou, S., Grubert, F., Zaugg, J. B., Kundaje, A., Liu, Y., Boyle, A. P., Zhang, Q. C., Zakharia, F., Spacek, D. V., et al. (2013) · Science · doi:10.1126/science.1242510 · Cited by 339
Self-citationWerling, D. M., Brand, H., An, J.-Y., Stone, M. R., Zhu, L., Glessner, J. T., Collins, R. L., Dong, S., Layer, R. M., Markenscoff-Papadimitriou, E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0107-y · Cited by 293
Harper, A. R., Goel, A., Grace, C., Thomson, K. L., Petersen, S. E., Xu, X., Waring, A., Ormondroyd, E., Kramer, C. M., Ho, C. Y., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00764-0 · Cited by 353
Inoue, F., Kircher, M., Martin, B., Cooper, G. M., Witten, D. M., McManus, M. T., Ahituv, N., & Shendure, J. (2017) · Genome Research · doi:10.1101/gr.212092.116 · Cited by 331
Loci associated with skin pigmentation identified in African populations
Crawford, N. G., Kelly, D. E., Hansen, M. E. B., Beltrame, M. H., Fan, S., Bowman, S. L., Jewett, E., Ranciaro, A., Thompson, S., Lo, Y., et al. (2017) · Science · doi:10.1126/science.aan8433 · Cited by 321
Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts
Undiagnosed Diseases Network, Frésard, L., Care4Rare Canada Consortium, Smail, C., Ferraro, N. M., Teran, N. A., Li, X., Smith, K. S., Bonner, D., Kernohan, K. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0457-8 · Cited by 321
Patsopoulos, N. A., Barcellos, L. F., Hintzen, R. Q., Schaefer, C., van Duijn, C. M., Noble, J. A., Raj, T., IMSGC, ANZgene, Gourraud, P.-A., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003926 · Cited by 312
Hill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., Gale, C. R., Davies, G., & Deary, I. J. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0001-5 · Cited by 311
CRISPR/Cas9-based engineering of the epigenome
Pulecio, J., Verma, N., Mejía-Ramírez, E., Huangfu, D., & Raya, A. (2017) · Cell Stem Cell · doi:10.1016/j.stem.2017.09.006 · Cited by 300
Orthotopic patient-derived xenografts of paediatric solid tumours
Stewart, E., Federico, S. M., Chen, X., Shelat, A. A., Bradley, C., Gordon, B., Karlstrom, A., Twarog, N. R., Clay, M. R., Bahrami, A., et al. (2017) · Nature · doi:10.1038/nature23647 · Cited by 286
Methods for ChIP-seq analysis: A practical workflow and advanced applications
Nakato, R., & Sakata, T. (2021) · Methods · doi:10.1016/j.ymeth.2020.03.005 · Cited by 267
De novo mutations in regulatory elements in neurodevelopmental disorders
Short, P. J., McRae, J. F., Gallone, G., Sifrim, A., Won, H., Geschwind, D. H., Wright, C. F., Firth, H. V., FitzPatrick, D. R., Barrett, J. C., et al. (2018) · Nature · doi:10.1038/nature25983 · Cited by 274
Denholtz, M., Bonora, G., Chronis, C., Splinter, E., de Laat, W., Ernst, J., Pellegrini, M., & Plath, K. (2013) · Cell Stem Cell · doi:10.1016/j.stem.2013.08.013 · Cited by 274
Self-citationEpigenetic encoding, heritability and plasticity of glioma transcriptional cell states
Chaligne, R., Gaiti, F., Silverbush, D., Schiffman, J. S., Weisman, H. R., Kluegel, L., Gritsch, S., Deochand, S. D., Gonzalez Castro, L. N., Richman, A. R., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00927-7 · Cited by 258
Epigenomics of human CD8 T cell differentiation and aging
Moskowitz, D. M., Zhang, D. W., Hu, B., Le Saux, S., Yanes, R. E., Ye, Z., Buenrostro, J. D., Weyand, C. M., Greenleaf, W. J., & Goronzy, J. J. (2017) · Science Immunology · doi:10.1126/sciimmunol.aag0192 · Cited by 252
Roayaei Ardakany, A., Gezer, H. T., Lonardi, S., & Ay, F. (2020) · Genome Biology · doi:10.1186/s13059-020-02167-0 · Cited by 250
Many human accelerated regions are developmental enhancers
Capra, J. A., Erwin, G. D., McKinsey, G., Rubenstein, J. L. R., & Pollard, K. S. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0025 · Cited by 235
KMT2D Deficiency Impairs Super-Enhancers to Confer a Glycolytic Vulnerability in Lung Cancer
Alam, H., Tang, M., Maitituoheti, M., Dhar, S. S., Kumar, M., Han, C. Y., Ambati, C. R., Amin, S. B., Gu, B., Chen, T.-Y., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.005 · Cited by 243
Chd8 mediates cortical neurogenesis via transcriptional regulation of cell cycle and Wnt signaling
Durak, O., Gao, F., Kaeser-Woo, Y. J., Rueda, R., Martorell, A. J., Nott, A., Liu, C. Y., Watson, L. A., & Tsai, L.-H. (2016) · Nature Neuroscience · doi:10.1038/nn.4400 · Cited by 243
Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks
Agarwal, V., & Shendure, J. (2018) · doi:10.1101/416685 · Cited by 243
“Also shown are gene annotations, CpG island calls, ChromHMM genomic state segmentation calls among two cell types (with predicted promoter hidden states colored in red), and genome-wide CAGE signal aggregated among many cell types (with red indicating signal from the positive strand and blue indicating signal from the…”
BackgroundPredicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks
Agarwal, V., & Shendure, J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107663 · Cited by 243
Hatoum, A. S., Colbert, S. M. C., Johnson, E. C., Huggett, S. B., Deak, J. D., Pathak, G. A., Jennings, M. V., Paul, S. E., Karcher, N. R., Hansen, I., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00034-y · Cited by 241
Marzi, S. J., Leung, S. K., Ribarska, T., Hannon, E., Smith, A. R., Pishva, E., Poschmann, J., Moore, K., Troakes, C., Al-Sarraj, S., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0253-7 · Cited by 232
Paternally inherited cis-regulatory structural variants are associated with autism
Brandler, W. M., Antaki, D., Gujral, M., Kleiber, M. L., Whitney, J., Maile, M. S., Hong, O., Chapman, T. R., Tan, S., Tandon, P., et al. (2018) · Science · doi:10.1126/science.aan2261 · Cited by 209
A statin-dependent QTL for GATM expression is associated with statin-induced myopathy
Mangravite, L. M., Engelhardt, B. E., Medina, M. W., Smith, J. D., Brown, C. D., Chasman, D. I., Mecham, B. H., Howie, B., Shim, H., Naidoo, D., et al. (2013) · Nature · doi:10.1038/nature12508 · Cited by 224
High-throughput functional testing of ENCODE segmentation predictions
Kwasnieski, J. C., Fiore, C., Chaudhari, H. G., & Cohen, B. A. (2014) · Genome Research · doi:10.1101/gr.173518.114 · Cited by 224
Ko, Y.-A., Mohtat, D., Suzuki, M., Park, A. S. D., Izquierdo, M. C., Han, S. Y., Kang, H. M., Si, H., Hostetter, T., Pullman, J. M., et al. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r108 · Cited by 225
Provençal, N., Arloth, J., Cattaneo, A., Anacker, C., Cattane, N., Wiechmann, T., Röh, S., Ködel, M., Klengel, T., Czamara, D., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820842116 · Cited by 209
Resolving systematic errors in widely used enhancer activity assays in human cells
Muerdter, F., Boryń, Ł. M., Woodfin, A. R., Neumayr, C., Rath, M., Zabidi, M. A., Pagani, M., Haberle, V., Kazmar, T., Catarino, R. R., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4534 · Cited by 208
Small, K. S., Todorčević, M., Civelek, M., El-Sayed Moustafa, J. S., Wang, X., Simon, M. M., Fernandez-Tajes, J., Mahajan, A., Horikoshi, M., Hugill, A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0088-x · Cited by 168
Quang, D., & Xie, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.020 · Cited by 204
Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205
Patterns of de novo tandem repeat mutations and their role in autism
Mitra, I., Huang, B., Mousavi, N., Ma, N., Lamkin, M., Yanicky, R., Shleizer-Burko, S., Lohmueller, K. E., & Gymrek, M. (2021) · Nature · doi:10.1038/s41586-020-03078-7 · Cited by 194
ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters
Bailey, S. D., Zhang, X., Desai, K., Aid, M., Corradin, O., Cowper-Sal·lari, R., Akhtar-Zaidi, B., Scacheri, P. C., Haibe-Kains, B., & Lupien, M. (2015) · Nature Communications · doi:10.1038/ncomms7186 · Cited by 194
An atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185
Cell-type-directed design of synthetic enhancers
Taskiran, I. I., Spanier, K. I., Dickmänken, H., Kempynck, N., Pančíková, A., Ekşi, E. C., Hulselmans, G., Ismail, J. N., Theunis, K., Vandepoel, R., et al. (2024) · Nature · doi:10.1038/s41586-023-06936-2 · Cited by 175
Nuclear stability and transcriptional directionality separate functionally distinct RNA species
Andersson, R., Refsing Andersen, P., Valen, E., Core, L. J., Bornholdt, J., Boyd, M., Heick Jensen, T., & Sandelin, A. (2014) · Nature Communications · doi:10.1038/ncomms6336 · Cited by 171
A supervised learning framework for chromatin loop detection in genome-wide contact maps
Salameh, T. J., Wang, X., Song, F., Zhang, B., Wright, S. M., Khunsriraksakul, C., Ruan, Y., & Yue, F. (2020) · Nature Communications · doi:10.1038/s41467-020-17239-9 · Cited by 165
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162
Self-citationSultana, T., van Essen, D., Siol, O., Bailly-Bechet, M., Philippe, C., Zine El Aabidine, A., Pioger, L., Nigumann, P., Saccani, S., Andrau, J.-C., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.02.036 · Cited by 154
Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D
Viñuela, A., Varshney, A., van de Bunt, M., Prasad, R. B., Asplund, O., Bennett, A., Boehnke, M., Brown, A. A., Erdos, M. R., Fadista, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18581-8 · Cited by 152
Gasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., Cusanovich, D. A., & Shendure, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.010 · Cited by 143
Global reorganisation of cis-regulatory units upon lineage commitment of human embryonic stem cells
Freire-Pritchett, P., Schoenfelder, S., Várnai, C., Wingett, S. W., Cairns, J., Collier, A. J., García-Vílchez, R., Furlan-Magaril, M., Osborne, C. S., Fraser, P., et al. (2017) · eLife · doi:10.7554/elife.21926 · Cited by 161
“We used ChromHMM (Ernst and Kellis, 2012) to integrate these histone marks and to define four combinatorial chromatin states in both ESCs and NECs, as follows: active (characterised by H3K4me3 and/or H3K27ac); Polycomb-associated (H3K27me3); poised (H3K4 methylation and H3K27me3); and background…”
MethodInfluentialMellén, M., Ayata, P., & Heintz, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708044114 · Cited by 151
Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells
Shipony, Z., Mukamel, Z., Cohen, N. M., Landan, G., Chomsky, E., Zeliger, S. R., Fried, Y. C., Ainbinder, E., Friedman, N., & Tanay, A. (2014) · Nature · doi:10.1038/nature13458 · Cited by 159
3DSNP: a database for linking human noncoding SNPs to their three-dimensional interacting genes
Lu, Y., Quan, C., Chen, H., Bo, X., & Zhang, C. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1022 · Cited by 133
Stewart, E., McEvoy, J., Wang, H., Chen, X., Honnell, V., Ocarz, M., Gordon, B., Dapper, J., Blankenship, K., Yang, Y., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.07.012 · Cited by 153
Integrative epigenetic taxonomy of primary prostate cancer
Stelloo, S., Nevedomskaya, E., Kim, Y., Schuurman, K., Valle-Encinas, E., Lobo, J., Krijgsman, O., Peeper, D. S., Chang, S. L., Feng, F. Y.-C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07270-2 · Cited by 154
Feichtinger, J., Hernández, I., Fischer, C., Hanscho, M., Auer, N., Hackl, M., Jadhav, V., Baumann, M., Krempl, P. M., Schmidl, C., et al. (2016) · Biotechnology and Bioengineering · doi:10.1002/bit.25990 · Cited by 148
Interpretation of deep learning in genomics and epigenomics
Talukder, A., Barham, C., Li, X., & Hu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa177 · Cited by 142
High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development
Wilderman, A., VanOudenhove, J., Kron, J., Noonan, J. P., & Cotney, J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.129 · Cited by 139
Zhang, L., Silva, T. C., Young, J. I., Gomez, L., Schmidt, M. A., Hamilton-Nelson, K. L., Kunkle, B. W., Chen, X., Martin, E. R., & Wang, L. (2020) · Nature Communications · doi:10.1038/s41467-020-19791-w · Cited by 139
Genome-wide analysis yields new loci associating with aortic valve stenosis
Helgadottir, A., Thorleifsson, G., Gretarsdottir, S., Stefansson, O. A., Tragante, V., Thorolfsdottir, R. B., Jonsdottir, I., Bjornsson, T., Steinthorsdottir, V., Verweij, N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03252-6 · Cited by 125
DEEP: a general computational framework for predicting enhancers
Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1058 · Cited by 125
Berest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K. D., Giles, H., Bruch, P.-M., Huber, W., Dietrich, S., Helin, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.106 · Cited by 119
DEEP Consortium, Salhab, A., Nordström, K., Gasparoni, G., Kattler, K., Ebert, P., Ramirez, F., Arrigoni, L., Müller, F., Polansky, J. K., et al. (2018) · Genome Biology · doi:10.1186/s13059-018-1510-5 · Cited by 115
Dogan, N., Wu, W., Morrissey, C. S., Chen, K.-B., Stonestrom, A., Long, M., Keller, C. A., Cheng, Y., Jain, D., Visel, A., et al. (2015) · Epigenetics & Chromatin · doi:10.1186/s13072-015-0009-5 · Cited by 114
Histone deacetylase 3 associates with MeCP2 to regulate FOXO and social behavior
Nott, A., Cheng, J., Gao, F., Lin, Y.-T., Gjoneska, E., Ko, T., Minhas, P., Zamudio, A. V., Meng, J., Zhang, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4347 · Cited by 110
Metadata
- Authors
- Ernst, J., & Kellis, M.
- Venue
- Nature Methods
- Published
- 2012-02-28
- Volume
- 9
- Issue
- 3
- Pages
- 215-216
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1598350571
- Cited by
- 2,475
- References
- 2
- FWCI
- 29.51
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
