ENCODE Database

ENCODE Database

Overview

The ENCODE (Encyclopedia of DNA Elements) Project has generated thousands of functional genomics experiments — TF ChIP-seq, ATAC-seq, DNase-seq, histone ChIP-seq, and RNA-seq — across 1000+ human and mouse cell types and tissues. The ENCODE Portal REST API provides structured JSON access to experiment metadata, file download URLs, and SCREEN cCRE (candidate cis-Regulatory Elements) annotations. All data is freely accessible without authentication for most endpoints.

When to Use

• Downloading TF ChIP-seq peak files (BED) for a specific transcription factor and cell type to annotate regulatory regions
• Finding ATAC-seq or DNase-seq peaks in a cell type to identify open chromatin regions near a gene of interest
• Retrieving cCREs (candidate cis-Regulatory Elements) overlapping a genomic region from ENCODE SCREEN
• Building reference regulatory tracks for variant annotation pipelines (e.g., annotating VCF variants against ENCODE peak sets)
• Exploring which experiments are available for a biosample (cell line, tissue, developmental stage) before planning a wet-lab experiment
• Querying all ChIP-seq experiments for a transcription factor across multiple cell types for comparative regulatory analysis
• Use regulomedb-database instead when you want pre-computed regulatory scores for specific SNPs — RegulomeDB integrates ENCODE data with eQTL and motif evidence into a single score
• Use deeptools-ngs-analysis instead when you have your own BAM files and need to generate bigWig coverage tracks; ENCODE database is for retrieving existing deposited data

Prerequisites

• Python packages: requests, pandas, matplotlib
• Data requirements: experiment accessions (e.g., ENCSR000AKC), biosample names (e.g., K562), TF target names (e.g., CTCF, TP53), or genomic regions (chr7:117548628-117748628)
• Environment: internet connection; no authentication required for public data; add Authorization: Bearer {api_key} header for submitter access
• Rate limits: no published hard limit; add time.sleep(0.5) for large batch queries to avoid connection resets

pip install requests pandas matplotlib

Quick Start

import requests

BASE = "https://www.encodeproject.org"

def search_experiments(assay="TF ChIP-seq", target="CTCF", biosample="K562", limit=5):
    """Find ENCODE experiments matching assay type, target, and biosample."""
    params = {
        "type": "Experiment",
        "assay_title": assay,