/regulatory-elements

name: regulatory-elements
description: Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.

Discover and Characterize Regulatory Elements with ENCODE

When to Use

• User wants to find enhancers, promoters, silencers, or insulators in a specific tissue using ENCODE
• User asks about "regulatory elements", "cCREs", "enhancer discovery", "ChromHMM", or "super-enhancers"
• User needs to classify chromatin states or identify active regulatory regions from histone mark data
• User wants to use ENCODE's 926,535 cCRE catalog or run functional validation (CRISPR/MPRA/reporter)
• Example queries: "find active enhancers in liver", "classify chromatin states for my tissue", "identify super-enhancers from H3K27ac data"

Identify, classify, and functionally characterize regulatory elements using ENCODE's catalog of 926,535 human candidate cis-regulatory elements (cCREs) and layered functional genomics data.

Scientific Rationale

**The question**: "What regulatory elements are active in my tissue of interest, and what are they doing?"

The human genome contains an estimated 1–2 million regulatory elements — far outnumbering the ~20,000 protein-coding genes. These elements (enhancers, promoters, silencers, insulators) control when, where, and how much each gene is expressed. No single biochemical assay can definitively identify a regulatory element; instead, combinatorial patterns of chromatin marks, accessibility, and TF binding are used to classify candidate elements.

The ENCODE cCRE Registry

The ENCODE Phase 3 project (ENCODE Project Consortium 2020) established a registry of **926,535 human and 339,815 mouse cCREs** covering 7.9% and 3.4% of their respective genomes. These are classified using combinations of DNase-seq, H3K4me3, H3K27ac, and CTCF ChIP-seq signals across hundreds of biosamples. The registry is accessible via the SCREEN web server and represents the most comprehensive catalog of candidate regulatory elements in any organism.

An expanded registry (Moore et al. 2024, bioRxiv preprint) extends this to **2.35 million human cCREs** with functional characterization from STARR-seq, MPRA, and CRISPR perturbation covering >90% of human cCREs.

Key Distinction: Candidate vs. Validated

ENCODE cCREs are **candidate** regulatory elements identified by biochemical signatures. Biochemical activity (histone marks, accessibility) is necessary but not sufficient for function. A region marked by H3K27ac is likely regulatory, but functional validation (perturbation, reporter assays) is required to confirm that it actually regulates a target gene. The gap between biochemical annotation and validated function is the central challenge.

Literature Support

• **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs. Introduces the SCREEN web server. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
• **Kundaje et al. 2015** (Nature, ~5,810 citations): Roadmap Epigenomics — integrative analysis of 111 reference human epigenomes. Chromatin state maps across tissues. Disease variants enriched in tissue-specific epigenomic marks. [DOI](https://doi.org/10.1038/nature14248)
• **Ernst & Kellis 2012** (Nature Methods, ~2,294 citations): ChromHMM — multivariate hidden Markov model for chromatin state discovery from combinatorial histone modification patterns. [DOI](https://doi.org/10.1038/nmeth.1906)
• **Hnisz et al. 2013** (Cell, ~3,215 citations): Super-enhancer catalog across human cell types. Disease-associated variation enriched in super-enhancers of disease-relevant cells. [DOI](https://doi.org/10.1016/j.cell.2013.09.053)
• **Whyte et al. 2013** (Cell, ~2,500 citations): Defined super-enhancers as large enhancer clusters occupied by master TFs and Mediator. Introduced the ROSE algorithm. [DOI](https://doi.org/10.1016/j.cell.2013.03.035)
• **Shlyueva et al. 2014** (Nature Reviews Genetics, ~1,200 citations): Authoritative review of enhancer sequence properties, chromatin signatures, genome-wide prediction, and high-throughput activity assays. [DOI](https://doi.org/10.1038/nrg3682)
• **Schoenfelder & Fraser 2019** (Nature Reviews Genetics, ~869 citations): How enhancer-promoter interactions are established through 3D genome architecture (TADs, CTCF loops). [DOI](https://doi.org/10.1038/s41576-019-0128-0)
• **Visel et al. 2007** (Nucleic Acids Research, ~1,079 citations): VISTA Enhancer Browser — in vivo transgenic mouse validation of enhancers. 4,500+ experiments. [DOI](https://doi.org/10.1093/nar/gkl822)
• **Gasperini et al. 2019** (Cell, ~465 citations): CRISPRi screen of 5,920 candidate enhancers with scRNA-seq readout. Identified 664 enhancer-gene pairs. Established the "crisprQTL" framework. [DOI](https://doi.org/10.1016/j.cell.2018.11.029)
• **Yao et al. 2024** (Nature Methods, ~26 citations): ENCODE4 Functional Characterization Centers — 108 CRISPRi screens, >540,000 perturbations. Pre-designed sgRNAs targeting 3.27M ENCODE SCREEN cCREs. [DOI](https://doi.org/10.1038/s41592-024-02216-7)
• **Nasser et al. 2021** (Nature, ~468 citations): ABC model enhancer-gene maps in 131 cell types. [DOI](https://doi.org/10.1038/s41586-021-03446-x)
• **Heintzman et al. 2007** (Nature Genetics, ~2,300 citations): Discovered that H3K4me1 marks enhancers while H3K4me3 marks promoters — the foundational chromatin signat