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Chromatin-state discovery and genome annotation with ChromHMM
Open accessAbstract
This protocol describes how to use ChromHMM, a robust open-source software package that enables the learning of chromatin states, annotates their occurrences across the genome, and facilitates their biological interpretation. Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.
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Cites
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
High-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
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-citationA bivalent chromatin structure marks key developmental genes in embryonic stem cells
Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716
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-citationThe accessible chromatin landscape of the human genome
Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
Combinatorial patterns of histone acetylations and methylations in the human genome
Wang, Z., Zang, C., Rosenfeld, J. A., Schones, D. E., Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Peng, W., Zhang, M. Q., et al. (2008) · Nature Genetics · doi:10.1038/ng.154 · Cited by 2,331
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-citationJavierre, 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-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
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-citationCooperative 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-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 · cited 1×
“ChromHMM has had applications to studying diseases such as Alzheimer’s disease 28,29 , neurodegeneration 30 , type 2 diabetes 31 , and cancer 32–35 , among many other diseases.”
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-citationGenome-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-citationZacher, B., Michel, M., Schwalb, B., Cramer, P., Tresch, A., & Gagneur, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169249 · Cited by 91
Libbrecht, M. W., Ay, F., Hoffman, M. M., Gilbert, D. M., Bilmes, J. A., & Noble, W. S. (2015) · Genome Research · doi:10.1101/gr.184341.114 · Cited by 84
Cited by
Schubach, M., Maass, T., Nazaretyan, L., Röner, S., & Kircher, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad989 · Cited by 455
Robertson, C. C., Inshaw, J. R. J., Onengut-Gumuscu, S., Chen, W.-M., Santa Cruz, D. F., Yang, H., Cutler, A. J., Crouch, D. J. M., Farber, E., Bridges, S. L., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00880-5 · Cited by 302
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301
Identification of significant chromatin contacts from HiChIP data by FitHiChIP
Bhattacharyya, S., Chandra, V., Vijayanand, P., & Ay, F. (2019) · Nature Communications · doi:10.1038/s41467-019-11950-y · Cited by 238
Kircher, M., Xiong, C., Martin, B., Schubach, M., Inoue, F., Bell, R. J. A., Costello, J. F., Shendure, J., & Ahituv, N. (2019) · Nature Communications · doi:10.1038/s41467-019-11526-w · Cited by 234
Metadata
- Authors
- Ernst, J., & Kellis, M.
- Venue
- Nature Protocols
- Published
- 2017-11-09
- Volume
- 12
- Issue
- 12
- Pages
- 2478-2492
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2768013244
- Cited by
- 910
- References
- 21
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- green
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