Using JASPAR Transcription Factor Binding Profiles with ENCODE ChIP-seq Data

Integrate JASPAR position weight matrices (PWMs) with ENCODE ChIP-seq peaks to validate TF binding targets, discover co-binding partners, and scan regulatory elements for TF binding potential.

Scientific Rationale

The question: "Does the expected TF binding motif appear in my ENCODE ChIP-seq peaks, and what other TF motifs are enriched?"

ENCODE TF ChIP-seq experiments identify where a transcription factor binds in the genome, but the peak coordinates alone do not confirm direct DNA binding or reveal the binding sequence specificity. JASPAR provides curated position weight matrices (PWMs) — mathematical representations of TF binding preferences — that enable two critical analyses:

1. Target validation: If CTCF ChIP-seq peaks are enriched for the CTCF motif (JASPAR MA0139.1), the experiment worked correctly. If they are NOT enriched, something may be wrong with the antibody, crosslinking, or peak calling.

2. Co-factor discovery: Motif enrichment analysis in ChIP-seq peaks often reveals motifs for co-binding TFs that were not the ChIP target, uncovering regulatory complexes.

What JASPAR Provides

• 900+ curated TF binding profiles across 7 taxonomic groups
• Position Frequency Matrices (PFMs), Position Weight Matrices (PWMs), and sequence logos
• Multiple profile versions reflecting binding mode diversity
• Quality scores (based on validation evidence)
• Taxonomic classification and TF structural class annotation
• REST API for programmatic access

The ENCODE-JASPAR Synergy

Key Literature

• Castro-Mondragon et al. 2022 "JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles" (Nucleic Acids Research, ~1,400 citations). The current JASPAR release with 1,956 profiles across 7 taxonomic groups, including unvalidated (UNVALIDATED collection) profiles. Introduced TFBSTools integration and improved REST API. DOI: 10.1093/nar/gkab1113
• Sandelin et al. 2004 "JASPAR: an open-access database for eukaryotic transcription factor binding profiles" (Nucleic Acids Research, ~2,000 citations). The founding JASPAR publication establishing the curated, open-access model for TF binding profiles. DOI: 10.1093/nar/gkh012
• Grant et al. 2011 "FIMO: scanning for occurrences of a given motif" (Bioinformatics, ~2,500 citations). FIMO (Find Individual Motif Occurrences) — the standard tool for scanning sequences with PWMs. Part of the MEME Suite. DOI: 10.1093/bioinformatics/btr064
• Heinz et al. 2010 "Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities" (Molecular Cell, ~5,000 citations). Introduced HOMER motif analysis — the most widely used tool for de novo and known motif enrichment in ChIP-seq peaks. DOI: 10.1016/j.molcel.2010.05.004
• ENCODE Project Consortium 2020 (Nature, ~1,656 citations). The TF ChIP-seq experiments that JASPAR motifs validate and enrich. DOI: 10.1038/s41586-020-2493-4

When to Use This Skill

JASPAR REST API Reference

Base URL: https://jaspar.genereg.net/api/v1/

No authentication required. Responses are JSON.

Key Endpoints

Common Matrix IDs for ENCODE TFs

Step 1: Retrieve ENCODE ChIP-seq Peaks

# Find TF ChIP-seq experiments
encode_search_experiments(
    assay_title="TF ChIP-seq",
    target="CTCF",
    organ="pancreas",
    biosample_type="tissue"
)

# Get IDR thresholded peaks (highest confidence)
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38",
    preferred_default=True
)

Track the experiment:

encode_track_experiment(accession="ENCSR...", notes="CTCF ChIP-seq for motif analysis")

Step 2: Get JASPAR Motif Profiles

Query by TF Name

import requests

def get_jaspar_matrix(tf_name, tax_group="vertebrates", collection="CORE"):
    """Get JASPAR matrix for a TF."""
    url = "https://jaspar.genereg.net/api/v1/matrix/"
    params = {
        "name": tf_name,
        "tax_group": tax_group,
        "collection": collection,
        "format": "json"
    }
    response = requests.get(url, params=params)
    results = response.json()["results"]
    if results:
        # Return the highest-version profile
        return sorted(results, key=lambda x: x["version"], reverse=True)[0]
    return None

ctcf_profile = get_jaspar_matrix("CTCF")
print(f"ID: {ctcf_profile['matrix_id']}, Version: {ctcf_profile['version']}")

Get the Position Frequency Matrix (PFM)

def get_pfm(matrix_id):
    """Get Position Frequency Matrix from JASPAR."""
    url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"
    params = {"format": "json"}
    response = requests.get(url, params=params)
    data = response.json()
    pfm = data["pfm"]
    # pfm is a dict with keys A, C, G, T, each a list of counts per position
    return pfm

pfm = get_pfm("MA0139.1")
print(f"Motif length: {len(pfm['A'])} bp")
for base in ["A", "C", "G", "T"]:
    print(f"{base}: {pfm[base]}")

Get MEME Format (for FIMO Scanning)

def get_meme_format(matrix_id):
    """Get motif in MEME format for use with FIMO."""
    url = f"htt