RegulomeDB Database

Overview

RegulomeDB integrates large-scale functional genomics data (ENCODE, Roadmap Epigenomics) to score genetic variants for regulatory potential. Each variant receives a ranking from 1a (highest regulatory confidence: eQTL + TF + DNase + motif + chromatin) to 7 (no known regulatory function). The v2 API is exposed as GET https://regulomedb.org/regulome-search/; the legacy POST /regulome-search/, POST /regulome-summary/, and GET /regulome-datasets/ JSON endpoints are no longer functional (return regulome-notfound stubs or 500). Access is free and requires no authentication.

When to Use

• Prioritizing GWAS hits for regulatory follow-up — identify which SNPs land in active regulatory elements
• Annotating a VCF or variant list with regulatory scores to filter to functionally relevant variants
• Identifying which transcription factors bind near a variant of interest (via the @graph evidence rows)
• Checking whether a non-coding variant overlaps a QTL and active chromatin simultaneously (features.QTL)
• Retrieving all annotated rsIDs in a genomic region for cis-regulatory analysis (region query with nearby_snps)
• Use clinvar-database instead when you need clinical pathogenicity classifications; RegulomeDB scores regulatory function, not germline disease association
• Use gwas-database instead when you want published GWAS associations with traits

Prerequisites

• Python packages: requests, pandas, matplotlib
• Data requirements: rsIDs (e.g., rs4946036), genomic positions (chr1:1000000), or region coordinates (chr1:1000000-2000000)
• Genome build: GRCh38 (default) or GRCh37; specify in all requests
• Rate limits: No published rate limits; use time.sleep(0.3) between requests in batch workflows

pip install requests pandas matplotlib

Quick Start

import requests

BASE = "https://regulomedb.org"

def regulome_score(variant, genome="GRCh38"):
    """Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=30,
    )
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    vs = d.get("variants", [])
    return {
        "query": variant,
        "ranking": rs.get("ranking"),           # 1a / 1b / ... / 7
        "probability": float(rs.get("probability", 0)),
        "rsids": vs[0].get("rsids") if vs else [],
        "chrom": vs[0].get("chrom") if vs else None,
        "pos": vs[0].get("start") if vs else None,
    }

print(regulome_score("rs4946036"))
# {'query': 'rs4946036', 'ranking': '7', 'probability': 0.18412,
#  'rsids': ['rs4946036'], 'chrom': 'chr6', 'pos': 114819799}

Core API

Query 1: Score a Single Variant (rsID or position)

The GET /regulome-search/ endpoint accepts an rsID or coordinate as regions=. Returns a regulome_score block (probability, ranking, tissue-specific scores) plus features flags and the per-dataset @graph evidence rows.

import requests

BASE = "https://regulomedb.org"

def score_variant(variant, genome="GRCh38"):
    """Return the regulome_score block and resolved coordinates."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=30,
    )
    r.raise_for_status()
    d = r.json()
    rs = d.get("regulome_score", {})
    vs = d.get("variants", [])
    feats = d.get("features", {})
    print(f"Variant   : {variant}")
    print(f"Resolved  : {vs[0]['chrom']}:{vs[0]['start']} ({', '.join(vs[0].get('rsids', []))})")
    print(f"Ranking   : {rs.get('ranking')}  prob={rs.get('probability')}")
    print(f"Features  : ChIP={feats['ChIP']} Chromatin_accessibility={feats['Chromatin_accessibility']} "
          f"QTL={feats['QTL']} Footprint={feats['Footprint']} PWM_matched={feats['PWM_matched']}")
    return d

# Strong-regulatory locus example
score_variant("chr11:5226739-5226740")
# Ranking: 1a (HBB beta-globin promoter, multi-evidence)

# Score by chromosomal position alone
score_variant("chr17:7670000-7670001")  # TP53 region

Query 2: Region Scan — List Annotated Variants in a Window

A range query returns up to limit resolved variants (variants[]) and all @graph evidence rows in the window, plus nearby_snps (rsIDs adjacent to the resolved hits).

import requests, pandas as pd

BASE = "https://regulomedb.org"

def scan_region(chrom, start, end, genome="GRCh38", limit=200):
    """List variants in a region with their resolved positions and overlapping rsIDs."""
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": f"{chrom}:{start}-{end}", "genome": genome,
                "format": "json", "limit": limit},
        timeout=60,
    )
    r.raise_for_status()
    d = r.json()
    variants = d.get("variants", [])
    print(f"Variants in {chrom}:{start}-{end}: {len(variants)} (total indexed = {d.get('total')})")
    rows = [{"rsids": ", ".join(v.get("rsids", [])),
             "chrom": v.get("chrom"),
             "start": v.get("start"),
             "end": v.get("end")} for v in variants]
    return pd.DataFrame(rows)

df = scan_region("chr11", 5226000, 5227000)
print(df.head(10).to_string(index=False))

Query 3: Full Evidence — Parse the @graph Rows

Each @graph[i] row is one experimental piece of evidence overlapping the query. Fields: method, target_label, biosample_ontology{term_name, organ_slims, classification}, dataset, file, value, chrom, start, end, strand, ancestry, disease_term_name.

import requests, pandas as pd

BASE = "https://regulomedb.org"

def evidence_rows(variant, genome="GRCh38"):
    r = requests.get(
        f"{BASE}/regulome-search/",
        params={"regions": variant, "genome": genome, "format": "json"},
        timeout=60,
    )
    r.raise_for_status()
    g = r.json().get("@graph", [])
    rows = []
    for row in g:
        bs = row.get("biosample_ontology") or {}
        rows.append({
            "method": row.get("method"),
            "target_label": row.get("target_label"),
            "biosample": bs.get("term_name"),
            "organ_slims": ", ".join(bs.get("organ_slims") or []),
            "dataset": row.get("dataset", "").split("/")[-2] if row.get("dataset") else None,
            "value": row.get("value"),
        })
    return pd.DataFrame(rows)

df_evidence = evidence_rows("chr11:5226739-5226740")
# Each method is one of: ChIP-seq, Histone ChIP-seq, ATAC-seq, DNase-seq,
# footprints, PWMs, chromatin state, eQTLs
print(df_evidence["method"].value_counts())

Query 4: TF ChIP-seq Hits — Filter Evidence by Method

To list the transcription factors binding near a variant, filter @graph rows where method == "ChIP-seq" and read target_label + biosample_ontology.term_name.

import requests, pandas as pd

BASE = "https://regulomedb.org"

def tf_binding(variant, genome="GRCh38"):
    r = requests.get(f"{BASE}/regulome-search/",
                     params={"regions": variant, "genome": genome, "format": "json"},
                     timeout=60)
    r.raise_for_status()
    rows = []
    for g in r.json().get("@graph", []):
        if g.get("method") != "ChIP-seq":
            continue
        bs = g.get("biosample_ontology") or {}
        rows.append({
            "tf": g.get("target_label"),
            "biosample": bs.get("term_name"),
            "classification": bs.get("classification"),
        })
    return pd.DataFrame(rows)

df_tfs = tf_binding("chr11:5226739-5226740")
print(f"TF ChIP-seq peaks overlapping query: {len(df_tfs)}")
print(df_tfs.groupby("tf").size().sort_values(ascending=False).head(10))

Query 5: Tissue-Specific Regulatory Score

regulome_score.tissue_specific_scores maps ~50 tissues to per-tissue regulat