JASPAR Database

Overview

JASPAR (https://jaspar.elixir.no/) is the gold-standard open-access database of curated, non-redundant transcription factor (TF) binding profiles stored as position frequency matrices (PFMs). JASPAR 2024 contains 1,210 non-redundant TF binding profiles for 164 eukaryotic species. Each profile is experimentally derived (ChIP-seq, SELEX, HT-SELEX, protein binding microarray, etc.) and rigorously validated.

Key resources:

• JASPAR portal: https://jaspar.elixir.no/

• REST API: https://jaspar.elixir.no/api/v1/

• API docs: https://jaspar.elixir.no/api/v1/docs/

• Python package: jaspar (via Biopython) or direct API

When to Use This Skill

Use JASPAR when:

• TF binding site prediction: Scan a DNA sequence for potential binding sites of a TF

• Regulatory variant interpretation: Does a GWAS/eQTL variant disrupt a TF binding motif?

• Promoter/enhancer analysis: What TFs are predicted to bind to a regulatory element?

• Gene regulatory network construction: Link TFs to their target genes via motif scanning

• TF family analysis: Compare binding profiles across a TF family (e.g., all homeobox factors)

• ChIP-seq analysis: Find known TF motifs enriched in ChIP-seq peaks

• ENCODE/ATAC-seq interpretation: Match open chromatin regions to TF binding profiles

Core Capabilities

1. JASPAR REST API

Base URL: https://jaspar.elixir.no/api/v1/

import requests

BASE_URL = "https://jaspar.elixir.no/api/v1"

def jaspar_get(endpoint, params=None): url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() return response.json()

2. Search for TF Profiles

def search_jaspar( tf_name=None, species=None, collection="CORE", tf_class=None, tf_family=None, page=1, page_size=25 ): """Search JASPAR for TF binding profiles.""" params = { "collection": collection, "page": page, "page_size": page_size, "format": "json" } if tf_name: params["name"] = tf_name if species: params["species"] = species # Use taxonomy ID or name, e.g., "9606" for human if tf_class: params["tf_class"] = tf_class if tf_family: params["tf_family"] = tf_family

return jaspar_get("matrix", params)

Examples:

Search for human CTCF profile

ctcf = search_jaspar("CTCF", species="9606") print(f"Found {ctcf['count']} CTCF profiles")

Search for all homeobox TFs in human

hox_tfs = search_jaspar(tf_class="Homeodomain", species="9606")

Search for a TF family

nfkb = search_jaspar(tf_family="NF-kappaB")

3. Fetch a Specific Matrix (PFM/PWM)

def get_matrix(matrix_id): """Fetch a specific JASPAR matrix by ID (e.g., 'MA0139.1' for CTCF).""" return jaspar_get(f"matrix/{matrix_id}/")

Example: Get CTCF matrix

ctcf_matrix = get_matrix("MA0139.1")

Matrix structure:

{

"matrix_id": "MA0139.1",

"name": "CTCF",

"collection": "CORE",

"tax_group": "vertebrates",

"pfm": { "A": [...], "C": [...], "G": [...], "T": [...] },

"consensus": "CCGCGNGGNGGCAG",

"length": 19,

"species": [{"tax_id": 9606, "name": "Homo sapiens"}],

"class": ["C2H2 zinc finger factors"],

"family": ["BEN domain factors"],

"type": "ChIP-seq",

"uniprot_ids": ["P49711"]

}

4. Download PFM/PWM as Matrix

import numpy as np

def get_pwm(matrix_id, pseudocount=0.8): """ Fetch a PFM from JASPAR and convert to PWM (log-odds). Returns numpy array of shape (4, L) in order A, C, G, T. """ matrix = get_matrix(matrix_id) pfm = matrix["pfm"]

# Convert PFM to numpy
pfm_array = np.array([pfm["A"], pfm["C"], pfm["G"], pfm["T"]], dtype=float)

# Add pseudocount
pfm_array += pseudocount

# Normalize to get PPM
ppm = pfm_array / pfm_array.sum(axis=0, keepdims=True)

# Convert to PWM (log-odds relative to background 0.25)
background = 0.25
pwm = np.log2(ppm / background)

return pwm, matrix["name"]

Example

pwm, name = get_pwm("MA0139.1") # CTCF print(f"PWM for {name}: shape {pwm.shape}") max_score = pwm.max(axis=0).sum() print(f"Maximum possible score: {max_score:.2f} bits")

5. Scan a DNA Sequence for TF Binding Sites

import numpy as np from typing import List, Tuple

NUCLEOTIDE_MAP = {'A': 0, 'C': 1, 'G': 2, 'T': 3, 'a': 0, 'c': 1, 'g': 2, 't': 3}

def scan_sequence(sequence: str, pwm: np.ndarray, threshold_pct: float = 0.8) -> List[dict]: """ Scan a DNA sequence for TF binding sites using a PWM.

Args:
    sequence: DNA sequence string
    pwm: PWM array (4 x L) in ACGT order
    threshold_pct: Fraction of max score to use as threshold (0-1)

Returns:
    List of hits with position, score, and matched sequence
"""
motif_len = pwm.shape[1]
max_score = pwm.max(axis=0).sum()
min_score = pwm.min(axis=0).sum()
threshold = min_score + threshold_pct * (max_score - min_score)

hits = []
seq = sequence.upper()

for i in range(len(seq) - motif_len + 1):
    subseq = seq[i:i + motif_len]
    # Skip if contains non-ACGT
    if any(c not in NUCLEOTIDE_MAP for c in subseq):
        continue

    score = sum(pwm[NUCLEOTIDE_MAP[c], j] for j, c in enumerate(subseq))

    if score >= threshold:
        relative_score = (score - min_score) / (max_score - min_score)
        hits.append({
            "position": i + 1,  # 1-based
            "score": score,
            "relative_score": relative_score,
            "sequence": subseq,
            "strand": "+"
        })

return hits

Example: Scan a promoter sequence for CTCF binding sites

promoter = "AGCCCGCGAGGNGGCAGTTGCCTGGAGCAGGATCAGCAGATC" pwm, name = get_pwm("MA0139.1") hits = scan_sequence(promoter, pwm, threshold_pct=0.75) for hit in hits: print(f" Position {hit['position']}: {hit['sequence']} (score: {hit['score']:.2f}, {hit['relative_score']:.0%})")

6. Scan Both Strands

def reverse_complement(seq: str) -> str: complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C', 'N': 'N'} return ''.join(complement.get(b, 'N') for b in reversed(seq.upper()))

def scan_both_strands(sequence: str, pwm: np.ndarray, threshold_pct: float = 0.8): """Scan forward and reverse complement strands.""" fwd_hits = scan_sequence(sequence, pwm, threshold_pct) for h in fwd_hits: h["strand"] = "+"

rev_seq = reverse_complement(sequence)
rev_hits = scan_sequence(rev_seq, pwm, threshold_pct)
seq_len = len(sequence)
for h in rev_hits:
    h["strand"] = "-"
    h["position"] = seq_len - h["position"] - len(h["sequence"]) + 2  # Convert to fwd coords

all_hits = fwd_hits + rev_hits
return sorted(all_hits, key=lambda x: x["position"])

7. Variant Impact on TF Binding

def variant_tfbs_impact(ref_seq: str, alt_seq: str, pwm: np.ndarray, tf_name: str, threshold_pct: float = 0.7): """ Assess impact of a SNP on TF binding by comparing ref vs alt sequences. Both sequences should be centered on the variant with flanking context. """ ref_hits = scan_both_strands(ref_seq, pwm, threshold_pct) alt_hits = scan_both_strands(alt_seq, pwm, threshold_pct)

max_ref = max((h["score"] for h in ref_hits), default=None)
max_alt = max((h["score"] for h in alt_hits), default=None)

result = {
    "tf": tf_name,
    "ref_max_score": max_ref,
    "alt_max_score": max_alt,
    "ref_has_site": len(ref_hits) > 0,
    "alt_has_site": len(alt_hits) > 0,
}
if max_ref and max_alt:
    result["score_change"] = max_alt - max_ref
    result["effect"] = "gained" if max_alt > max_ref else "disrupted"
elif max_ref and not max_alt:
    result["effect"] = "disrupted"
elif not max_ref and max_alt:
    result["effect"] = "gained"
else:
    result["effect"] = "no_site"

return result

Query Workflows

Workflow 1: Find All TF Binding Sites in a Promoter

import requests, numpy as np

1. Get relevant TF matrices (e.g., all human TFs in CORE collection)

response = requests.get( "https://jaspar.elixir.no/api/v1/matrix/", params={"species": "9606", "collection": "CORE", "page_size": 500, "page": 1} ) matrices = response.json()["results"]

2. For each matrix, compute PWM and scan promoter

promoter = "CCCGCCCGCCCGCCGCCCGCAGTTAATGAGCCCAGCGTGCC" # Example

all_hits = [] for m in matrices[:10]: # Limit for demo pwm_data = requests.get(f"https://jaspar.elixir.no/api/v1/matrix/{m['matrix_id']}/").json() pfm = pfm_data["pfm"] pfm_arr = np.array([pfm["A"], pfm["C"], pfm["G"], pfm["T"]], dtype=float) + 0.8 ppm = pfm_arr / pfm_arr.sum(axis=0) pwm = np.log2(ppm / 0.25)

hits = scan_sequence(promoter, pwm, threshold_pct=0.8)
for h in hits:
    h["tf_name"] = m["name"]
    h["matrix_id"] = m["matrix_id"]
all_hits.extend(hits)

print(f"Found {len(all_hits)} TF binding sites") for h in sorted(all_hits, key=lambda x: -x["score"])[:5]: print(f" {h['tf_name']} ({h['matrix_id']}): pos {h['position']}, score {h['score']:.2f}")

Workflow 2: SNP Impact on TF Binding (Regulatory Variant Analysis)

• Retrieve the genomic sequence flanking the SNP (±20 bp each side)

• Construct ref and alt sequences

• Scan with all relevant TF PWMs

• Report TFs whose binding is created or destroyed by the SNP

Workflow 3: Motif Enrichment Analysis

• Identify a set of peak sequences (e.g., from ChIP-seq or ATAC-seq)

• Scan all peaks with JASPAR PWMs

• Compare hit rates in peaks vs. background sequences

• Report significantly enriched motifs (Fisher's exact test or FIMO-style scoring)

Collections Available

Collection Description Profiles

CORE

Non-redundant, high-quality profiles ~1,210

UNVALIDATED

Experimentally derived but not validated ~500

PHYLOFACTS

Phylogenetically conserved sites ~50

CNE

Conserved non-coding elements ~30

POLII

RNA Pol II binding profiles ~20

FAM

TF family representative profiles ~170

SPLICE

Splice factor profiles ~20

Best Practices

• Use CORE collection for most analyses — best validated and non-redundant

• Threshold selection: 80% of max score is common for de novo prediction; 90% for high-confidence

• Always scan both strands — TFs can bind in either orientation

• Provide flanking context for variant analysis: at least (motif_length - 1) bp on each side

• Consider background: PWM scores relative to uniform (0.25) background; adjust for actual GC content

• Cross-validate with ChIP-seq data when available — motif scanning has many false positives

• Use Biopython's motifs module for full-featured scanning: from Bio import motifs

Additional Resources

• JASPAR portal: https://jaspar.elixir.no/

• API documentation: https://jaspar.elixir.no/api/v1/docs/

• JASPAR 2024 paper: Castro-Mondragon et al. (2022) Nucleic Acids Research. PMID: 34875674

• Biopython motifs: https://biopython.org/docs/latest/Tutorial/chapter_motifs.html

• FIMO tool (for large-scale scanning): https://meme-suite.org/meme/tools/fimo

• HOMER (motif enrichment): http://homer.ucsd.edu/homer/

• GitHub: https://github.com/wassermanlab/JASPAR-UCSC-tracks