jaspar-database — База данных профилей связывания транскрипционных факторов

--- name: jaspar-database description: Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation. license: CC0-1.0 metadata: skill-author: Kuan-lin Huang --- # 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/` ```python 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 ```python 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) ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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) 1. Retrieve the genomic sequence flanking the SNP (±20 bp each side) 2. Construct ref and alt sequences 3. Scan with all relevant TF PWMs 4. Report TFs whose binding is created or destroyed by the SNP ### Workflow 3: Motif Enrichment Analysis 1. Identify a set of peak sequences (e.g., from ChIP-seq or ATAC-seq) 2. Scan all peaks with JASPAR PWMs 3. Compare hit rates in peaks vs. background sequences 4. 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