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
Items 101–155 of 155
Large-scale chromatin reorganization reactivates placenta-specific genes that drive cellular aging
Liu, Z., Ji, Q., Ren, J., Yan, P., Wu, Z., Wang, S., Sun, L., Wang, Z., Li, J., Sun, G., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.05.004 · Cited by 109
Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
Sethi, A., Gu, M., Gumusgoz, E., Chan, L., Yan, K.-K., Rozowsky, J., Barozzi, I., Afzal, V., Akiyama, J. A., Plajzer-Frick, I., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0907-8 · Cited by 109
Zacher, B., Michel, M., Schwalb, B., Cramer, P., Tresch, A., & Gagneur, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169249 · Cited by 91
Integrative genomic analysis reveals widespread enhancer regulation by p53 in response to DNA damage
Younger, S. T., Kenzelmann-Broz, D., Jung, H., Attardi, L. D., & Rinn, J. L. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv284 · Cited by 99
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104
Baranasic, D., Hörtenhuber, M., Balwierz, P. J., Zehnder, T., Mukarram, A. K., Nepal, C., Várnai, C., Hadzhiev, Y., Jimenez-Gonzalez, A., Li, N., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01089-w · Cited by 101
PEDLA: predicting enhancers with a deep learning-based algorithmic framework
Liu, F., Li, H., Ren, C., Bo, X., & Shu, W. (2016) · Scientific Reports · doi:10.1038/srep28517 · Cited by 101
Analytical tools and current challenges in the modern era of neuroepigenomics
Maze, I., Shen, L., Zhang, B., Garcia, B. A., Shao, N., Mitchell, A., Sun, H., Akbarian, S., Allis, C. D., & Nestler, E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3816 · Cited by 100
Vockley, C. M., Guo, C., Majoros, W. H., Nodzenski, M., Scholtens, D. M., Hayes, M. G., Lowe, W. L., & Reddy, T. E. (2015) · Genome Research · doi:10.1101/gr.190090.115 · Cited by 99
Prediction of enhancer-promoter interactions via natural language processing
Zeng, W., Wu, M., & Jiang, R. (2018) · BMC Genomics · doi:10.1186/s12864-018-4459-6 · Cited by 96
“Promoter and enhancer regions were identified using ENCODE Segway [26] and ChromHMM [27] annotations for K562, GM12878, HeLa-S3, and HUVEC cell lines, and using Roadmap [28] Epige-nomics ChromHMM annotations for NHEK and IMR90 cell lines.”
MethodACF chromatin-remodeling complex mediates stress-induced depressive-like behavior
Sun, H., Damez-Werno, D. M., Scobie, K. N., Shao, N.-Y., Dias, C., Rabkin, J., Koo, J. W., Korb, E., Bagot, R. C., Ahn, F. H., et al. (2015) · Nature Medicine · doi:10.1038/nm.3939 · Cited by 91
LEDGF and HDGF2 relieve the nucleosome-induced barrier to transcription in differentiated cells
LeRoy, G., Oksuz, O., Descostes, N., Aoi, Y., Ganai, R. A., Kara, H. O., Yu, J.-R., Lee, C.-H., Stafford, J., Shilatifard, A., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay3068 · Cited by 89
DamC reveals principles of chromatin folding in vivo without crosslinking and ligation
Redolfi, J., Zhan, Y., Valdes-Quezada, C., Kryzhanovska, M., Guerreiro, I., Iesmantavicius, V., Pollex, T., Grand, R. S., Mulugeta, E., Kind, J., et al. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0231-0 · Cited by 86
Seki, R., Li, C., Fang, Q., Hayashi, S., Egawa, S., Hu, J., Xu, L., Pan, H., Kondo, M., Sato, T., et al. (2017) · Nature Communications · doi:10.1038/ncomms14229 · Cited by 84
Yotova, I., Hsu, E., Do, C., Gaba, A., Sczabolcs, M., Dekan, S., Kenner, L., Wenzl, R., & Tycko, B. (2017) · PLOS ONE · doi:10.1371/journal.pone.0170859 · Cited by 81
Maricque, B. B., Chaudhari, H. G., & Cohen, B. A. (2019) · Nature Biotechnology · doi:10.1038/nbt.4285 · Cited by 83
Transcriptional enhancers and their communication with gene promoters
Ray-Jones, H., & Spivakov, M. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03903-w · Cited by 81
Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Burren, O. S., Rubio García, A., Javierre, B.-M., Rainbow, D. B., Cairns, J., Cooper, N. J., Lambourne, J. J., Schofield, E., Castro Dopico, X., Ferreira, R. C., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1285-0 · Cited by 80
Serpeloni, F., Radtke, K. M., Hecker, T., Sill, J., Vukojevic, V., Assis, S. G. D., Schauer, M., Elbert, T., & Nätt, D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00269 · Cited by 77
Dissecting Murine Muscle Stem Cell Aging through Regeneration Using Integrative Genomic Analysis
Shcherbina, A., Larouche, J., Fraczek, P., Yang, B. A., Brown, L. A., Markworth, J. F., Chung, C. H., Khaliq, M., de Silva, K., Choi, J. J., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107964 · Cited by 76
Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping
Kikuchi, M., Hara, N., Hasegawa, M., Miyashita, A., Kuwano, R., Ikeuchi, T., & Nakaya, A. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0574-8 · Cited by 74
“Z, and DNase I-hypersensitive sites) using ChromHMM and ChromImpute [23, 24].”
BackgroundMethodGain of CTCF-Anchored Chromatin Loops Marks the Exit from Naive Pluripotency
Pękowska, A., Klaus, B., Xiang, W., Severino, J., Daigle, N., Klein, F. A., Oleś, M., Casellas, R., Ellenberg, J., Steinmetz, L. M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.09.003 · Cited by 74
The cis-regulatory effects of modern human-specific variants
Weiss, C. V., Harshman, L., Inoue, F., Fraser, H. B., Petrov, D. A., Ahituv, N., & Gokhman, D. (2021) · eLife · doi:10.7554/elife.63713 · Cited by 70
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. (2021) · doi:10.1101/2021.04.22.440891 · Cited by 67
Yen, A., & Kellis, M. (2015) · Nature Communications · doi:10.1038/ncomms8973 · Cited by 67
Self-citationSex-specific DNA methylation differences in Alzheimer’s disease pathology
Zhang, L., Young, J. I., Gomez, L., Silva, T. C., Schmidt, M. A., Cai, J., Chen, X., Martin, E. R., & Wang, L. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01177-8 · Cited by 58
“Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization.”
MethodAtypical plant homeodomain of UBR7 functions as an H2BK120Ub ligase and breast tumor suppressor
Adhikary, S., Chakravarti, D., Terranova, C., Sengupta, I., Maitituoheti, M., Dasgupta, A., Srivastava, D. K., Ma, J., Raman, A. T., Tarco, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08986-5 · Cited by 57
Subramanian, S., Thoms, J. A. I., Huang, Y., Cornejo-Páramo, P., Koch, F. C., Jacquelin, S., Shen, S., Song, E., Joshi, S., Brownlee, C., et al. (2023) · Blood · doi:10.1182/blood.2023021120 · Cited by 57
Garcia-Gomez, A., Li, T., de la Calle-Fabregat, C., Rodríguez-Ubreva, J., Ciudad, L., Català-Moll, F., Godoy-Tena, G., Martín-Sánchez, M., San-Segundo, L., Muntión, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20715-x · Cited by 57
Vojinovic, D., Adams, H. H., Jian, X., Yang, Q., Smith, A. V., Bis, J. C., Teumer, A., Scholz, M., Armstrong, N. J., Hofer, E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06234-w · Cited by 52
Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S., & Schaid, D. J. (2016) · Genetics · doi:10.1534/genetics.116.188953 · Cited by 52
Burns, A. M., Farinelli-Scharly, M., Hugues-Ascery, S., Sanchez-Mut, J. V., Santoni, G., & Gräff, J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116797119 · Cited by 49
Klein, H.-U., Bennett, D. A., & De Jager, P. L. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1612-7 · Cited by 49
Zheng, J., Wheeler, E., Pietzner, M., Andlauer, T. F. M., Yau, M. S., Hartley, A. E., Brumpton, B. M., Rasheed, H., Kemp, J. P., Frysz, M., et al. (2023) · Arthritis & Rheumatology · doi:10.1002/art.42538 · Cited by 44
Hsieh, T.-H. S., Cattoglio, C., Slobodyanyuk, E., Hansen, A. S., Darzacq, X., & Tjian, R. (2021) · doi:10.1101/2021.07.14.452365 · Cited by 42
Widespread Chromatin Accessibility at Repetitive Elements Links Stem Cells with Human Cancer
Gomez, N. C., Hepperla, A. J., Dumitru, R., Simon, J. M., Fang, F., & Davis, I. J. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.011 · Cited by 41
Universal chromatin state annotation of the mouse genome
Vu, H., & Ernst, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02994-x · Cited by 37
Self-citationGenome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways
Jansen, P. R., Watanabe, K., Stringer, S., Skene, N., Bryois, J., Hammerschlag, A. R., de Leeuw, C. A., Benjamins, J., Muñoz-Manchado, A. B., Nagel, M., et al. (2018) · doi:10.1101/214973 · Cited by 860
Integrative Genetic and Epigenetic Analysis Uncovers Regulatory Mechanisms of Autoimmune Disease
Shooshtari, P., Huang, H., & Cotsapas, C. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.001 · Cited by 33
Ray-Jones, H., Duffus, K., McGovern, A., Martin, P., Shi, C., Hankinson, J., Gough, O., Yarwood, A., Morris, A. P., Adamson, A., et al. (2020) · BMC Biology · doi:10.1186/s12915-020-00779-3 · Cited by 31
An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Fang, R., Qiu, Y., Poirion, O., Li, B., Liu, H., et al. (2020) · doi:10.1101/2020.05.10.087585 · Cited by 187
Accelerated evolution of enhancer hotspots in the mammal ancestor
Holloway, A. K., Bruneau, B. G., Sukonnik, T., Rubenstein, J. L., & Pollard, K. S. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msv344 · Cited by 27
Cheung, K., Barter, M. J., Falk, J., Proctor, C. J., Reynard, L. N., & Young, D. A. (2020) · The FASEB Journal · doi:10.1096/fj.201902061rr · Cited by 23
Fu, K., Chronis, C., Soufi, A., Bonora, G., Edwards, M., Smale, S. T., Zaret, K. S., Plath, K., & Pellegrini, M. (2018) · BMC Genomics · doi:10.1186/s12864-018-5326-1 · Cited by 23
Fernandez-Jimenez, N., Fore, R., Cilleros-Portet, A., Lepeule, J., Perron, P., Kvist, T., Tian, F.-Y., Lesseur, C., Binder, A. M., Lozano, M., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04267-y · Cited by 20
Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform
Madrigal, P., & Krajewski, P. (2015) · BioData Mining · doi:10.1186/s13040-015-0051-7 · Cited by 15
Zhao, W., Yuan, W., Dong, T., Qi, W., Feng, Z., Li, C., & Sun, Y. (2025) · Communications Biology · doi:10.1038/s42003-025-08160-2 · Cited by 16
Ang, D. A., Carter, J.-M., Deka, K., Tan, J. H. L., Zhou, J., Chen, Q., Chng, W. J., Harmston, N., & Li, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-46728-4 · Cited by 13
Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states
Li, W. V., Razaee, Z. S., & Li, J. J. (2016) · BMC Genomics · doi:10.1186/s12864-015-2303-9 · Cited by 10
MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size
Lafferty, M. J., Aygün, N., Patel, N. K., Krupa, O., Liang, D., Wolter, J. M., Geschwind, D. H., de la Torre-Ubieta, L., & Stein, J. L. (2023) · eLife · doi:10.7554/elife.79488 · Cited by 7
An integrative approach for fine-mapping chromatin interactions
Jaroszewicz, A., & Ernst, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz843 · Cited by 7
Self-citationWu, Q., Yang, F., An, Q., Liu, Y., Tian, H., Du, Y., Cai, W., Lei, M., Xu, J., Deng, L., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.126.049987
Song, W., Shi, Y., & Lin, G. N. (2024) · doi:10.7554/elife.92574.2
Song, W., Shi, Y., & Lin, G. N. (2024) · eLife · doi:10.7554/elife.92574
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.
