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

The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.

The Ensembl Regulatory Build

Open access
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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Crossref · 10.1186/s13059-015-0621-5

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

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Metadata

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
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
Licence
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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