The Ensembl Regulatory Build

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

400 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3ef2c48b28eecc5bab4beef25870e74f1958c342

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Ensembl 2016 (2015, methodology): “The re-designed method, which was deployed last year on 18 human cell lines, has been extended to mouse and covers 8 cell lines and tissues (8).”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, methodology): “p10) (Zerbino et al. 2015) using CellBase to extract the feature ID and type.”
Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data (2016, background): “For example, splice sites were very highly constrained (median LINSIGHT score of 0.956, indicating a 95.6% probability of fitness consequences due to mutations at these nucleotide sites), whereas annotated TFBSs showed reduced, but still substantial, constraint (median LINSIGHT score of 0.240 for TF”
Ensembl 2023 (2022, background): “The Ensembl Regulatory Build (23) now consists of 622 461 regulatory features across 118 epigenomes.”
Ensembl 2019 (2018, methodology): “These data were integrated into the mouse version of the Ensembl Regulatory Build (23), which increased our coverage from 8 to 79 cell types and tissues, many of which are mapped to specific developmental stages and therefore create an important dataset to track epigenetic modifications during devel”
Ensembl regulation resources (2016, methodology): “We therefore remap all of the original data onto the current reference genome, call peaks and normalise the signal with our uniform pipeline (33).”
The UCSC Genome Browser database: 2019 update (2018, background): “Examples include cis-regulatory annotations created by JASPAR (3), ReMap (4), the Eukaryotic Promoter Database (5), ENCODE(6), the Ensembl regulatory build (7) and Brain and Roadmap Epigenomics (8), RNAs from FANTOM5 (9), Mouse Alleles from MGI (10), lncRNAs from LNCipedia (11), Cancer genomics (sho”
A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. (2016, background): “57 the chromosomal topological domain, 31 rather than simply taking the closest gene.”
GeneHancer: genome-wide integration of enhancers and target genes in GeneCards (2017, background): “The mapping program requires access to catalogues of promoters (e.g. Refs 19, 60, 61) as well as of enhancers, of which GeneHancer is an example.”
Ensembl 2017 (2016, methodology): “The increase is largely based on datasets from the IHEC member projects BLUEPRINT (23) and Roadmap Epigenomics (24), which were uniformly annotated using the Ensembl Regulatory Build methodology (25).”
Ensembl 2020 (2019, methodology): “Our analysis methods are capable of annotating genes and transcripts (5), performing comparative genomics (6), integrating variation data sets from diverse resources (7) and annotating regulatory activity (8) to create a comprehensive and consistent baseline of reference annotation.”