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Related Experiment Video

Updated: Apr 14, 2026

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

Published on: July 18, 2025

The ensembl regulatory build.

Daniel R Zerbino¹, Steven P Wilder², Nathan Johnson³

¹European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. zerbino@ebi.ac.uk.

|April 19, 2015

Summary

This summary is machine-generated.

Most disease-associated genomic variants are in non-coding regulatory regions. We created an intuitive summary of human genome regulatory regions using public epigenetic and transcription factor binding data, aiding variant interpretation.

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Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

Genomic variants linked to traits/diseases often reside in non-coding regulatory regions, complicating interpretation.
Understanding these regulatory regions is crucial for deciphering the genetic basis of complex traits and diseases.

Purpose of the Study:

To construct an intuitive summary of regulatory regions in the human genome.
To facilitate the interpretation of non-coding genomic variants associated with phenotypic traits and diseases.

Main Methods:

Collected public data on epigenetic marks (e.g., histone modifications, DNA methylation) across various human cell types.
Integrated transcription factor binding data to identify active regulatory elements.
Developed a comprehensive regulatory build for the human genome.

Main Results:

An intuitive summary of human genome regulatory regions was successfully constructed.
The Ensembl Regulatory Build was validated against independent experimental assays for sensitivity.
The build provides a framework for interpreting the functional impact of non-coding variants.

Conclusions:

The Ensembl Regulatory Build offers a valuable resource for understanding the role of regulatory regions in human biology and disease.
Continued enrichment with new data will enhance its utility for genomic variant interpretation.
This resource is freely accessible, promoting broader research in the field.