Cited by 450Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyEpigenetics and DNA Methylation

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The Ensembl Regulatory Build

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Zerbino, Daniel R, Wilder, Steven P, Johnson, Nathan, Juettemann, Thomas, Flicek, Paul R·2015·Genome Biology

DOI 10.1186/s13059-015-0621-5PMC PMC4407537

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Abstract

Most genomic variants associated with phenotypic traits or disease do not fall within gene coding regions, but in regulatory regions, rendering their interpretation difficult. We collected public data on epigenetic marks and transcription factor binding in human cell types and used it to construct an intuitive summary of regulatory regions in the human genome. We verified it against independent assays for sensitivity. The Ensembl Regulatory Build will be progressively enriched when more data is made available. It is freely available on the Ensembl browser, from the Ensembl Regulation MySQL database server and in a dedicated track hub.

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Figure 1 The Regulatory Build process. In a first step we run segmentation software across multiple cell types. For each cell type and at each base pair, the genome is assigned a state, identified by an arbitrary number assigned by the segmentation software. We assign to each state a non-unique functional label, represented by its color on the browser, as shown at the top. For each state at each base pair, we compute the number of cell types sharing that state at that position, as shown in the center of the figure. Having selected relevant states and set some thresholds, we define regions of interest, which are the foundation of the regulatory build. These regions are then complemented with unannotated ChIP-Seq transcription factor binding site peaks and unannotated DNase1 hypersensitivity sites.CC BY · PubMed Central

Figure 2 Experimental marks associated with different labels. This heatmap represents the experimental marks and the label associated with each state. The states were defined by Segway, and the labels assigned by the Ensembl Regulatory Build a posteriori. Although the label assignment relies mainly on overlaps with known features, the states with the same labels co-cluster based on their experimental marks. The main exception are the promoter flanking states, which cluster either with promoters or with distal cis-regulatory elements. In effect, these states tend to represent a mixture of the other two.CC BY · PubMed Central

Figure 3 Decision tree assigning labels to unsupervised segmentation states.CC BY · PubMed Central

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Self-citation
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Rappaport, N., Fishilevich, S., Nudel, R., Twik, M., Belinky, F., Plaschkes, I., Stein, T. I., Cohen, D. R., Oz-Levi, D., Safran, M., et al. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0359-2 · Cited by 93
RETRACTED: Senescent Stromal Cells Promote Cancer Resistance through SIRT1 Loss-Potentiated Overproduction of Small Extracellular Vesicles
Han, L., Long, Q., Li, S., Xu, Q., Zhang, B., Dou, X., Qian, M., Jiramongkol, Y., Guo, J., Cao, L., et al. (2020) · Cancer Research · doi:10.1158/0008-5472.can-20-0506 · Cited by 93
Retracted
A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers
Glodzik, D., Morganella, S., Davies, H., Simpson, P. T., Li, Y., Zou, X., Diez-Perez, J., Staaf, J., Alexandrov, L. B., Smid, M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3771 · Cited by 84
MTCH2-mediated mitochondrial fusion drives exit from naïve pluripotency in embryonic stem cells
Bahat, A., Goldman, A., Zaltsman, Y., Khan, D. H., Halperin, C., Amzallag, E., Krupalnik, V., Mullokandov, M., Silberman, A., Erez, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07519-w · Cited by 90
Increased enhancer–promoter interactions during developmental enhancer activation in mammals
Chen, Z., Snetkova, V., Bower, G., Jacinto, S. H., Clock, B., Dizehchi, A., Barozzi, I., Mannion, B. J., Alcaina‐Caro, A., López-Ríos, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01681-2 · Cited by 98
Genetics of rheumatic fever and rheumatic heart disease
Muhamed, B., Parks, T., & Sliwa, K. S. (2019) · Nature Reviews Cardiology · doi:10.1038/s41569-019-0258-2 · Cited by 78
Defining the genetic and evolutionary architecture of alternative splicing in response to infection
Rotival, M., Quach, H., & Quintana‐Murci, L. (2019) · Nature Communications · doi:10.1038/s41467-019-09689-7 · Cited by 77
High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations
Zhang, Y., Chen, F., Fonseca, N. A., He, Y., Fujita, M., Nakagawa, H., Zhang, Z., Brāzma, A., PCAWG Transcriptome Working Group, Amin, S. B., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13885-w · Cited by 75
Ensembl regulation resources
Zerbino, D. R., Johnson, N., Juettemann, T., Sheppard, D., Wilder, S. P., Lavidas, I., Nuhn, M., Perry, E. H., Raffaillac-Desfosses, Q., Sobral, D., et al. (2016) · Database · doi:10.1093/database/bav119 · Cited by 76
Self-citation
Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms
Wright, C. F., Quaife, N. M., Ramos-Hernández, L., Danecek, P., Ferla, M. P., Samocha, K. E., Kaplanis, J., Gardner, E. J., Eberhardt, R. Y., Chao, K. R., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.04.025 · Cited by 71
Genomic benchmarks: a collection of datasets for genomic sequence classification
(2023) · doi:10.1186/s12863-023-01123-8 · Cited by 74
Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
Scherer, M., Nebel, A., Franke, A., Walter, J., Lengauer, T., Bock, C., Müller, F., & List, M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa120 · Cited by 66
DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data
Foreman, J., Perrett, D., Mazaika, E., Hunt, S. E., Ware, J. S., & Firth, H. V. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-102822-100509 · Cited by 76
Asparaginase-associated pancreatitis: a study on phenotype and genotype in the NOPHO ALL2008 protocol
Wolthers, B. O., Frandsen, T. L., Abrahamsson, J., Albertsen, B. K., Helt, L. R., Heyman, M. M., Jónsson, Ó. G., Kõrgvee, L. T., Lund, B., Raja, R. A., et al. (2016) · Leukemia · doi:10.1038/leu.2016.203 · Cited by 68
Chromatin dysregulation associated with NSD1 mutation in head and neck squamous cell carcinoma
Farhangdoost, N., Horth, C., Hu, B., Bareke, E., Chen, X., Li, Y., Coradin, M., Garcia, B. A., Lü, C., & Majewski, J. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108769 · Cited by 67
Aryl hydrocarbon receptor signaling modifies Toll-like receptor-regulated responses in human dendritic cells
Kado, S. Y., Chang, W. L. W., Nguyen, A., Wolny, M., Shepherd, D. M., & Vogel, C. F. A. (2016) · Archives of Toxicology · doi:10.1007/s00204-016-1880-y · Cited by 68
Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus
Zocher, S., Overall, R. W., Lesche, M., Dahl, A., & Kempermann, G. (2021) · Nature Communications · doi:10.1038/s41467-021-23993-1 · Cited by 69
Caloric restriction mitigates age-associated hippocampal differential CG and non-CG methylation
Hadad, N., Unnikrishnan, A., Jackson, J. A., Masser, D. R., Otalora, L., Stanford, D. R., Richardson, A. G., & Freeman, W. M. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.03.009 · Cited by 66
HEDD: Human Enhancer Disease Database
Wang, Z., Zhang, Q., Zhang, W., Lin, J.-R., Cai, Y., Mitra, J., & Zhang, Z. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx988 · Cited by 62
Histone modifications underlie monocyte dysregulation in patients with systemic sclerosis, underlining the treatment potential of epigenetic targeting
van der Kroef, M., Castellucci, M., Mokrý, M., Cossu, M., Garonzi, M., Bossini‐Castillo, L., Chouri, E., Wichers, C. G. K., Beretta, L., Trombetta, E., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-214295 · Cited by 62
Transgenic Overexpression of Aryl Hydrocarbon Receptor Repressor (AhRR) and AhR-Mediated Induction of CYP1A1, Cytokines, and Acute Toxicity
Vogel, C. F. A., Chang, W. L. W., Kado, S. Y., McCulloh, K. L., Vogel, H., Wu, D., Haarmann‐Stemmann, T., Yang, G., Leung, P. S. C., Matsumura, F., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510194 · Cited by 57
Chromatin loop anchors are associated with genome instability in cancer and recombination hotspots in the germline
Kaiser, V., & Semple, C. (2017) · Genome Biology · doi:10.1186/s13059-018-1483-4 · Cited by 52
Conserved regions in long non-coding RNAs contain abundant translation and protein–RNA interaction signatures
Ruiz‐Orera, J., & Albà, M. M. (2019) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqz002 · Cited by 58
High-throughput characterization of the role of non-B DNA motifs on promoter function
Georgakopoulos‐Soares, I., Victorino, J., Parada, G. E., Agarwal, V., Zhao, J., Wong, H. Y., Umar, M. I., Elor, O., Muhwezi, A., An, J., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100111 · Cited by 58
Applications of the 1000 Genomes Project resources
Zheng-Bradley, X., & Flicek, P. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw027 · Cited by 61
Self-citation
The Extracellular Matrix Receptor Discoidin Domain Receptor 1 Regulates Collagen Transcription by Translocating to the Nucleus
Chiusa, M., Hu, W., Liao, H., Su, Y. R., Borza, C. M., de Caestecker, M. P., Skrypnyk, N. I., Fogo, A. B., Pedchenko, V. K., Li, X., et al. (2019) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018111160 · Cited by 57
Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen, C. H., Margolis, M. P., Dai, R., Zhang, P., Przytycki, P. F., Vo, D., Bhattacharya, A., Matoba, N., Tang, M., Jiao, C., et al. (2024) · Science · doi:10.1126/science.adh0829 · Cited by 65
Towards a reference genome that captures global genetic diversity
Wong, K. H. Y., Ma, W., Wei, C.-Y., Yeh, E., Lin, W.-J., Wang, E. H. F., Su, J.-P., Hsieh, F., Kao, H., Chen, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19311-w · Cited by 55
De novo DNA methylation controls neuronal maturation during adult hippocampal neurogenesis
Zocher, S., Overall, R. W., Berdugo‐Vega, G., Rund, N., Karasinsky, A., Adusumilli, V. S., Steinhauer, C., Scheibenstock, S., Händler, K., Schultze, J. L., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2020107100 · Cited by 54
Transcriptional decomposition reveals active chromatin architectures and cell specific regulatory interactions
Rennie, S., Dalby, M., van Duin, L., & Andersson, R. (2018) · Nature Communications · doi:10.1038/s41467-017-02798-1 · Cited by 55
A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types
Libbrecht, M. W., Rodriguez, O. L., Weng, Z., Bilmes, J. A., Hoffman, M. M., & Noble, W. S. (2019) · Genome biology · doi:10.1186/s13059-019-1784-2 · Cited by 59
Identification of PIEZO1 polymorphisms for human bone mineral density
Bai, W.-Y., Wang, L., Ying, Z., Hu, B., Xu, L., Zhang, G., Cong, P., Zhu, X., Zou, W., & Zheng, H. (2020) · Bone · doi:10.1016/j.bone.2020.115247 · Cited by 50
Refgenie: a reference genome resource manager
Stolarczyk, M., Reuter, V. P., Smith, J. P., Magee, N. E., & Sheffield, N. C. (2020) · GigaScience · doi:10.1093/gigascience/giz149 · Cited by 50
Public data and open source tools for multi-assay genomic investigation of disease
Kannan, L., Ramos, M., Re, A., El-Hachem, N., Safikhani, Z., Gendoo, D. M. A., Davis, S. R., Gómez-Cabrero, D., Castelo, R., Hansen, K. D., et al. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv080 · Cited by 49
The Evolution of Gene-Specific Transcriptional Noise Is Driven by Selection at the Pathway Level
Barroso, G. V., Puzović, N., & Dutheil, J. Y. (2017) · Genetics · doi:10.1534/genetics.117.300467 · Cited by 46
Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease
Morrow, J. D., Cho, M. H., Platig, J., Zhou, X., DeMeo, D. L., Qiu, W., Celli, B., Marchetti, N. T., Criner, G. J., Bueno, R., et al. (2018) · Human Genomics · doi:10.1186/s40246-018-0132-z · Cited by 48
Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype
Ma, Y., Jun, G., Zhang, X., Chung, J., Naj, A. C., Chen, Y., Bellenguez, C., Hamilton‐Nelson, K. L., Martin, E. R., Kunkle, B. W., et al. (2019) · JAMA Neurology · doi:10.1001/jamaneurol.2019.1456 · Cited by 47

Authors: Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T., & Flicek, P. R.
Affiliations: European Bioinformatics Institute (GB)
Corresponding author: Zerbino, Daniel R
Flicek, Paul R
ORCID: Zerbino, Daniel R
Wilder, Steven P
Johnson, Nathan
Juettemann, Thomas
Flicek, Paul R
Venue: Genome Biology
Publisher: Springer Science and Business Media LLC
Published: 2015-03-24
Volume: 16
Issue: 1
Pages: 56-56
ISSN: 1465-6906 (ISSN-L)
DOI: 10.1186/s13059-015-0621-5
PMC: PMC4407537
OpenAlex: W2102659607
Cited by: 429
References: 53
Citations by year: 2026: 8 · 2025: 24 · 2024: 40 · 2023: 45 · 2022: 61 · 2021: 47 · 2020: 52 · 2019: 51 · 2018: 35 · 2017: 23 · 2016: 34 · 2015: 8
FWCI: 14.47
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Genome biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Wellcome Trust (FP7/2007–2013, 095908, WT095908, 223210)
European Molecular Biology Laboratory
European Commission (226073, 282510, 2007–2013, FP7 2007-2013, FP7/2007, 223210)
Directorate for Biological Sciences
Biotechnology and Biological Sciences Research Council (BB/I025360/2)
National Human Genome Research Institute (U01HG004695)
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: regulatory elements, epigenetic modifications, transcription factor binding, human genome annotation, non-coding variants, variant interpretation
MeSH terms: Humans, Software, Transcription Factors, User-Computer Interface, Computational Biology, Internet, Genomics, Databases, Genetic, Epigenesis, Genetic

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