Remap Database

ReMap Database

Overview

ReMap 2022 is an integrative database of transcription factor (TF), cofactor, and chromatin regulator binding sites derived from uniformly reprocessed ChIP-seq experiments. The 2022 release catalogs 165 million non-redundant peaks from 8,113 ChIP-seq datasets covering 1,210 TFs across human (hg38/hg19), mouse (mm10), Drosophila, and Arabidopsis genomes. All peaks are called with a consistent pipeline from public GEO/ArrayExpress experiments. Access is via the ReMap 2022 REST API at https://remap2022.univ-amu.fr/api/ and bulk BED file downloads; no authentication required.

When to Use

• Finding all TFs with ChIP-seq peaks overlapping a genomic region of interest (e.g., a GWAS SNP locus or candidate enhancer)
• Retrieving TF peaks near a gene's transcription start site to map its proximal regulatory landscape
• Listing all TFs available in ReMap for human or mouse with their peak and dataset counts
• Filtering ChIP-seq peaks by regulatory biotype annotation (promoter, enhancer, exon, intron, intergenic) for a TF in a specific cell line
• Downloading a BED file of all binding peaks for a TF across all cell types for offline analysis
• Identifying co-binding TFs at a locus by querying all overlapping peaks and grouping by TF name
• Use jaspar-database instead when you need PWM/PFM sequence models of TF binding specificity rather than ChIP-seq peak locations
• For ENCODE-specific regulatory tracks and accessibility data use encode-database; ReMap aggregates TF binding peaks from many sources including ENCODE

Prerequisites

• Python packages: requests, pandas, matplotlib
• Data requirements: genomic coordinates (GRCh38/hg38 or hg19), gene names, or TF names
• Environment: internet connection; no API key required
• Rate limits: no official published limits; use time.sleep(0.5) between batch requests to avoid server overload
• Note: The ReMap API is a research API; endpoint availability may vary. All examples include a BED download fallback.

bashCopy code
pip install requests pandas matplotlib

Quick Start

pythonCopy code
import requests

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

# Query TF peaks overlapping a genomic region
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={
    "chr": "chr17",
    "start": 7_670_000,
    "end": 7_690_000,
    "assembly": "hg38"
}, timeout=30)
r.raise_for_status()
peaks = r.json()
print(f"Peaks overlapping TP53 locus: {len(peaks)}")
tfs = set(p.get("name", "").split(":")[0] for p in peaks)
print(f"Unique TFs: {len(tfs)}")
print(f"TF names (first 10): {sorted(tfs)[:10]}")

Core API

Query 1: Region Overlap

Find all TF ChIP-seq peaks overlapping a specified genomic window. Returns peak records including TF name, cell type, coordinates, and score.

pythonCopy code
import requests, time, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def query_region(chrom, start, end, assembly="hg38", timeout=30):
    """Return all ReMap peaks overlapping [chrom:start-end]."""
    r = requests.get(f"{REMAP_API}/peaks/overlap/", params={
        "chr": chrom, "start": start, "end": end, "assembly": assembly
    }, timeout=timeout)
    r.raise_for_status()
    return r.json()

# Query 100 kb window on chr17 around TP53
peaks = query_region("chr17", 7_670_000, 7_690_000, assembly="hg38")
print(f"Total peaks: {len(peaks)}")

# Parse name field: format is "TF:experiment_id:cell_type"
rows = []
for p in peaks:
    parts = p.get("name", "::").split(":")
    tf   = parts[0] if len(parts) > 0 else ""
    exp  = parts[1] if len(parts) > 1 else ""
    cell = parts[2] if len(parts) > 2 else ""
    rows.append({
        "chr": p.get("chr", p.get("chrom", "")),
        "start": p.get("start", 0),
        "end": p.get("end", 0),
        "tf_name": tf,
        "experiment_id": exp,
        "cell_type": cell,
        "score": p.get("score", 0),
    })

df = pd.DataFrame(rows)
print(f"\nUnique TFs: {df['tf_name'].nunique()}")
print(f"Top TFs by peak count:\n{df['tf_name'].value_counts().head(10).to_string()}")

pythonCopy code
# Fallback: if API is unavailable, use a locally downloaded BED file
# Download from: https://remap2022.univ-amu.fr/download_page
# e.g., remap2022_all_macs2_hg38_v1_0.bed.gz

import pandas as pd

def query_region_from_bed(bed_file, chrom, start, end):
    """Filter a ReMap BED file for overlapping peaks."""
    cols = ["chr", "start", "end", "name", "score", "strand",
            "thick_start", "thick_end", "color"]
    df = pd.read_csv(bed_file, sep="\t", header=None, names=cols,
                     compression="infer")
    mask = (df["chr"] == chrom) & (df["end"] > start) & (df["start"] < end)
    return df[mask].reset_index(drop=True)

# Usage (requires downloaded BED):
# df = query_region_from_bed("remap2022_all_macs2_hg38_v1_0.bed.gz",
#                             "chr17", 7_670_000, 7_690_000)

Query 2: Gene-Centric Query

Retrieve all TF ChIP-seq peaks near a gene's TSS, providing a promoter-proximal regulatory landscape for the gene.

pythonCopy code
import requests, time, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def query_gene_peaks(gene_name, assembly="hg38", timeout=30):
    """Return all ReMap peaks near a gene TSS."""
    r = requests.get(f"{REMAP_API}/peaks/gene/", params={
        "gene": gene_name, "assembly": assembly
    }, timeout=timeout)
    r.raise_for_status()
    return r.json()

peaks = query_gene_peaks("MYC", assembly="hg38")
print(f"Peaks near MYC TSS: {len(peaks)}")

rows = []
for p in peaks:
    parts = p.get("name", "::").split(":")
    rows.append({
        "tf_name": parts[0] if parts else "",
        "cell_type": parts[2] if len(parts) > 2 else "",
        "chr": p.get("chr", p.get("chrom", "")),
        "start": p.get("start", 0),
        "end": p.get("end", 0),
        "score": p.get("score", 0),
        "biotype": p.get("biotype", ""),
    })

df = pd.DataFrame(rows)
print(f"\nTFs near MYC TSS ({df['tf_name'].nunique()} unique):")
print(df["tf_name"].value_counts().head(10).to_string())
print(f"\nCell types represented: {df['cell_type'].nunique()}")

Query 3: TF Browser

List all TFs available in ReMap for a given genome assembly, with peak and experiment counts.

pythonCopy code
import requests, time, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def list_tfs(assembly="hg38", timeout=30):
    """Return all TFs in ReMap for the given assembly with statistics."""
    r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout)
    r.raise_for_status()
    return r.json()

def get_database_stats(assembly="hg38", timeout=30):
    """Return overall database statistics for the assembly."""
    r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout)
    r.raise_for_status()
    return r.json()

# Database overview
try:
    stats = get_database_stats("hg38")
    print(f"ReMap 2022 hg38 statistics:")
    for k, v in stats.items():
        print(f"  {k}: {v}")
except Exception as e:
    print(f"Stats endpoint unavailable: {e}")
    print("ReMap 2022 hg38: 165M peaks, 1,210 TFs, 8,113 datasets (from publication)")

# TF list
try:
    tfs = list_tfs("hg38")
    df_tfs = pd.DataFrame(tfs)
    print(f"\nTFs available (hg38): {len(df_tfs)}")
    if "peak_count" in df_tfs.columns:
        top = df_tfs.nlargest(10, "peak_count")[["name", "peak_count", "dataset_count"]]
        print("Top 10 TFs by peak count:")
        print(top.to_string(index=False))
except Exception as e:
    print(f"TF list endpoint unavailable: {e}")
    print("Use TF name queries directly (Query 4) or download TF-specific BED files.")

Query 4: TF-Specific Peak Query

Retrieve all peaks for a named TF in a given assembly, optionally filtered by cell type.

pythonCopy code
import requests, time, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def query_tf_peaks(tf_name, assembly="hg38", timeout=30):
    """Return all ChIP-seq peaks for a TF across all cell types."""
    r = requests.get(f"{REMAP_API}/tfbs/name/", params={
        "name": tf_name, "assembly": assembly
    }, timeout=timeout)
    r.raise_for_status()
    return r.json()

peaks = query_tf_peaks("CTCF", assembly="hg38")
print(f"CTCF peaks (all cell types): {len(peaks)}")

# Parse and summarize
rows = []
for p in peaks:
    parts = p.get("name", "::").split(":")
    rows.append({
        "tf_name": parts[0] if parts else "",
        "cell_type": parts[2] if len(parts) > 2 else "",
        "chr":   p.get("chr",   p.get("chrom", "")),
        "start": p.get("start", 0),
        "end":   p.get("end",   0),
        "score": p.get("score", 0),
        "biotype": p.get("biotype", ""),
    })

df = pd.DataFrame(rows)
print(f"Cell types: {df['cell_type'].nunique()}")
print(f"Chromosomes: {df['chr'].nunique()}")
print(f"Peak width stats (bp):")
df["width"] = df["end"] - df["start"]
print(f"  Median: {df['width'].median():.0f}  Mean: {df['width'].mean():.0f}  "
      f"Min: {df['width'].min()}  Max: {df['width'].max()}")

Query 5: Biotype Filter and Regulatory Annotation

Filter peaks by regulatory biotype annotation to identify binding at promoters, enhancers, or intergenic regions.

pythonCopy code
import requests, pandas as pd, matplotlib.pyplot as plt

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def get_biotypes(assembly="hg38", timeout=30):
    """List all regulatory biotype categories available."""
    r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout)
    r.raise_for_status()
    return r.json()

def query_tf_by_biotype(tf_name, biotype, assembly="hg38", timeout=30):
    """Retrieve TF peaks filtered by regulatory biotype."""
    r = requests.get(f"{REMAP_API}/peaks/biotype/", params={
        "name": tf_name, "biotype": biotype, "assembly": assembly
    }, timeout=timeout)
    r.raise_for_status()
    return r.json()

# List available biotypes
try:
    biotypes = get_biotypes("hg38")
    print(f"Available biotypes: {biotypes}")
except Exception:
    biotypes = ["promoter", "enhancer", "exon", "intron", "intergenic", "UTR"]
    print(f"Using known biotypes: {biotypes}")

# Query CTCF peaks and plot biotype distribution
peaks = query_tf_peaks("CTCF", assembly="hg38")  # from Query 4 function above

def query_tf_peaks(tf_name, assembly="hg38", timeout=30):
    r = requests.get(f"https://remap2022.univ-amu.fr/api/v1/tfbs/name/",
                     params={"name": tf_name, "assembly": assembly}, timeout=timeout)
    r.raise_for_status()
    return r.json()

peaks = query_tf_peaks("CTCF")
rows = [{"biotype": p.get("biotype", "unknown"),
         "cell_type": p.get("name", "::").split(":")[2] if len(p.get("name","").split(":")) > 2 else ""}
        for p in peaks]
df = pd.DataFrame(rows)

biotype_counts = df["biotype"].value_counts()
biotype_counts = biotype_counts[biotype_counts > 0]
print(f"\nCTCF peak biotype distribution:")
print(biotype_counts.to_string())

# Stacked bar chart across top 5 cell types
top_cells = df["cell_type"].value_counts().head(5).index.tolist()
pivot = (df[df["cell_type"].isin(top_cells)]
         .groupby(["cell_type", "biotype"])
         .size()
         .unstack(fill_value=0))

fig, ax = plt.subplots(figsize=(9, 5))
pivot.plot(kind="bar", stacked=True, ax=ax, colormap="tab10", edgecolor="white")
ax.set_xlabel("Cell Type")
ax.set_ylabel("Peak Count")
ax.set_title("CTCF ChIP-seq Peak Biotype Distribution by Cell Type (ReMap 2022, hg38)")
ax.legend(title="Biotype", bbox_to_anchor=(1.01, 1), loc="upper left", fontsize=8)
plt.tight_layout()
plt.savefig("CTCF_biotype_distribution.png", dpi=150, bbox_inches="tight")
print("Saved CTCF_biotype_distribution.png")

Key Concepts

Peak Name Field Format

The name field in every ReMap peak record encodes three pieces of information as a colon-separated string:

Copy code
TF_NAME:EXPERIMENT_ID:CELL_TYPE

For example: CTCF:GSE30263.SRX028592:GM12878

Always parse with .split(":") and guard against missing parts. Some records may have fewer than three components if metadata is incomplete.

Assemblies

BED File Download (API Fallback)

When the REST API is unavailable or for offline bulk analysis, ReMap provides pre-built BED files at https://remap2022.univ-amu.fr/download_page. Key files:

• remap2022_all_macs2_hg38_v1_0.bed.gz — all peaks, hg38 (large, ~5 GB)
• remap2022_{TF}_macs2_hg38_v1_0.bed.gz — per-TF peak files
• remap2022_crm_macs2_hg38_v1_0.bed.gz — cis-regulatory modules (merged peaks)

pythonCopy code
import pandas as pd

def load_remap_bed(bed_path, chrom=None, start=None, end=None):
    """
    Load a ReMap BED file with optional region filter.
    Columns: chr, start, end, name (TF:exp:cell), score, strand,
             thick_start, thick_end, itemRgb
    """
    cols = ["chr", "start", "end", "name", "score", "strand",
            "thick_start", "thick_end", "itemRgb"]
    df = pd.read_csv(bed_path, sep="\t", header=None, names=cols,
                     compression="infer", low_memory=False)
    if chrom:
        df = df[df["chr"] == chrom]
    if start is not None and end is not None:
        df = df[(df["end"] > start) & (df["start"] < end)]
    # Parse name field
    parts = df["name"].str.split(":", expand=True)
    df["tf_name"]       = parts[0]
    df["experiment_id"] = parts[1] if 1 in parts.columns else ""
    df["cell_type"]     = parts[2] if 2 in parts.columns else ""
    return df.reset_index(drop=True)

# Usage example (offline):
# df = load_remap_bed("remap2022_CTCF_macs2_hg38_v1_0.bed.gz",
#                     chrom="chr17", start=7_670_000, end=7_690_000)
# print(df.head())

Common Workflows

Workflow 1: TF Co-occupancy Analysis at a Locus

Goal: Identify all TFs with ChIP-seq evidence at a genomic locus and rank by peak count, then export a co-occupancy matrix.

pythonCopy code
import requests, time, pandas as pd, matplotlib.pyplot as plt

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def query_region(chrom, start, end, assembly="hg38", timeout=30):
    r = requests.get(f"{REMAP_API}/peaks/overlap/", params={
        "chr": chrom, "start": start, "end": end, "assembly": assembly
    }, timeout=timeout)
    r.raise_for_status()
    return r.json()

def parse_peaks(peaks):
    rows = []
    for p in peaks:
        parts = p.get("name", "::").split(":")
        rows.append({
            "tf_name":  parts[0] if len(parts) > 0 else "unknown",
            "cell_type": parts[2] if len(parts) > 2 else "unknown",
            "chr":   p.get("chr",   p.get("chrom", "")),
            "start": p.get("start", 0),
            "end":   p.get("end",   0),
            "score": p.get("score", 0),
        })
    return pd.DataFrame(rows)

# BRCA1 promoter region (GRCh38)
peaks = query_region("chr17", 43_044_000, 43_050_000, assembly="hg38")
df = parse_peaks(peaks)
print(f"Peaks at BRCA1 promoter: {len(df)}")

# TF occupancy summary
tf_summary = (df.groupby("tf_name")
                .agg(peak_count=("tf_name", "count"),
                     cell_types=("cell_type", "nunique"),
                     mean_score=("score", "mean"))
                .sort_values("peak_count", ascending=False))
print(f"\nTop TFs at BRCA1 promoter:")
print(tf_summary.head(15).to_string())
tf_summary.to_csv("BRCA1_promoter_TF_occupancy.csv")

# Horizontal bar chart
top = tf_summary.head(20)
fig, ax = plt.subplots(figsize=(8, 6))
ax.barh(top.index[::-1], top["peak_count"][::-1], color="#1f77b4", edgecolor="white")
ax.set_xlabel("Number of ChIP-seq Peaks")
ax.set_title("TF Co-occupancy at BRCA1 Promoter (ReMap 2022, hg38)")
plt.tight_layout()
plt.savefig("BRCA1_promoter_TF_cooccupancy.png", dpi=150, bbox_inches="tight")
print("Saved BRCA1_promoter_TF_cooccupancy.png")

Workflow 2: Gene Regulatory Profile — TSS-Proximal TF Binding Atlas

Goal: For a list of genes, retrieve their promoter-proximal TF binding profiles and compare the TF repertoires across genes.

pythonCopy code
import requests, time, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def query_gene_peaks(gene_name, assembly="hg38", timeout=30):
    try:
        r = requests.get(f"{REMAP_API}/peaks/gene/", params={
            "gene": gene_name, "assembly": assembly
        }, timeout=timeout)
        r.raise_for_status()
        return r.json()
    except Exception as e:
        print(f"  Warning: {gene_name} failed — {e}")
        return []

genes_of_interest = ["MYC", "TP53", "BRCA1", "EGFR", "CDK4"]
gene_tf_profiles = {}

for gene in genes_of_interest:
    peaks = query_gene_peaks(gene, assembly="hg38")
    if peaks:
        tfs = set()
        for p in peaks:
            parts = p.get("name", "").split(":")
            if parts:
                tfs.add(parts[0])
        gene_tf_profiles[gene] = tfs
        print(f"{gene}: {len(peaks)} peaks, {len(tfs)} unique TFs")
    time.sleep(0.5)

# Build binary TF presence matrix
all_tfs = sorted(set().union(*gene_tf_profiles.values()))
matrix = pd.DataFrame(
    {gene: [1 if tf in gene_tf_profiles.get(gene, set()) else 0 for tf in all_tfs]
     for gene in genes_of_interest},
    index=all_tfs
)
print(f"\nTF × Gene matrix: {matrix.shape}")
print(f"TFs shared by all genes: {(matrix.sum(axis=1) == len(genes_of_interest)).sum()}")
matrix.to_csv("gene_TF_binding_atlas.csv")
print("Saved gene_TF_binding_atlas.csv")

Workflow 3: Download and Analyze TF Peak BED File

Goal: Download a TF-specific ReMap BED file and analyze its genomic distribution with pandas.

pythonCopy code
import requests, gzip, io, pandas as pd, time

# ReMap provides per-TF BED files. For large-scale offline analysis:
REMAP_DOWNLOAD_BASE = "https://remap2022.univ-amu.fr/storage/remap2022/hg38/MACS2"

def download_tf_bed(tf_name, assembly="hg38", save_path=None):
    """
    Attempt to download TF-specific BED file from ReMap.
    Falls back to API region query if download unavailable.
    """
    filename = f"remap2022_{tf_name}_macs2_{assembly}_v1_0.bed.gz"
    url = f"{REMAP_DOWNLOAD_BASE}/{filename}"
    print(f"Attempting download: {url}")
    r = requests.get(url, stream=True, timeout=60)
    if r.status_code == 200:
        if save_path:
            with open(save_path, "wb") as f:
                for chunk in r.iter_content(chunk_size=8192):
                    f.write(chunk)
            print(f"Saved: {save_path}")
            return save_path
        else:
            # Read directly into DataFrame
            content = b"".join(r.iter_content(chunk_size=8192))
            cols = ["chr", "start", "end", "name", "score", "strand",
                    "thick_start", "thick_end", "itemRgb"]
            with gzip.open(io.BytesIO(content), "rt") as gz:
                df = pd.read_csv(gz, sep="\t", header=None, names=cols)
            return df
    else:
        print(f"Download returned {r.status_code}; use API query as fallback")
        return None

# Analyze a downloaded BED file
def analyze_remap_bed(df):
    """Compute summary statistics for a ReMap peak DataFrame."""
    parts = df["name"].str.split(":", expand=True)
    df = df.copy()
    df["tf_name"]   = parts[0]
    df["cell_type"] = parts[2] if 2 in parts.columns else "unknown"
    df["width"] = df["end"] - df["start"]

    print(f"Total peaks: {len(df):,}")
    print(f"Unique TFs: {df['tf_name'].nunique()}")
    print(f"Unique cell types: {df['cell_type'].nunique()}")
    print(f"\nPeak width (bp): median={df['width'].median():.0f}  "
          f"mean={df['width'].mean():.0f}  range=[{df['width'].min()}, {df['width'].max()}]")
    print(f"\nChromosome distribution:")
    chr_counts = df["chr"].value_counts().head(5)
    print(chr_counts.to_string())
    return df

# Example usage (requires BED download or substitute with API results):
# df_raw = download_tf_bed("CTCF", save_path="CTCF_hg38.bed.gz")
# if df_raw is not None:
#     df_analyzed = analyze_remap_bed(df_raw)

Key Parameters

Best Practices

1. Parse the name field defensively: The TF:experiment:cell_type format may have fewer than three components for some records. Always guard with parts[n] if len(parts) > n else "".

2. Use BED downloads for genome-wide analyses: Querying large genomic regions or all peaks for a TF via the REST API can time out. For whole-genome or per-chromosome scans, download the per-TF or per-assembly BED files from the ReMap download page and filter locally with pandas or bedtools.

3. Cross-reference with JASPAR for sequence evidence: ReMap peaks show where TF binding was detected by ChIP-seq (positional evidence); JASPAR PWMs show what sequence the TF prefers (motif evidence). For robust regulatory annotation, require both: a ReMap peak in the region AND a JASPAR motif hit within the peak.

4. Use time.sleep(0.5) in batch loops: The ReMap API serves a research community; polite request pacing prevents throttling.

5. Validate assembly coordinates: ReMap 2022 hg38 peaks use 0-based half-open BED coordinates ([start, end)). When comparing with VCF or 1-based GFF coordinates, add 1 to start.

Common Recipes

Recipe: Find TFs Binding at a GWAS SNP

When to use: Prioritize functional candidates from a GWAS hit by identifying which TFs bind at the SNP location.

pythonCopy code
import requests

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def tfs_at_snp(chrom, pos, window=500, assembly="hg38"):
    """Find TFs with ChIP-seq peaks overlapping a SNP position ± window bp."""
    r = requests.get(f"{REMAP_API}/peaks/overlap/", params={
        "chr": chrom, "start": pos - window, "end": pos + window,
        "assembly": assembly
    }, timeout=30)
    r.raise_for_status()
    peaks = r.json()
    tfs = {}
    for p in peaks:
        parts = p.get("name", "::").split(":")
        tf = parts[0] if parts else "unknown"
        tfs[tf] = tfs.get(tf, 0) + 1
    return dict(sorted(tfs.items(), key=lambda x: -x[1]))

# Example: rs2736100 (TERT locus, chr5:1,286,401)
snp_tfs = tfs_at_snp("chr5", 1_286_401, window=500, assembly="hg38")
print(f"TFs at TERT GWAS SNP (±500 bp): {len(snp_tfs)}")
for tf, count in list(snp_tfs.items())[:10]:
    print(f"  {tf:<20s} {count:3d} peaks")

Recipe: Compare TF Binding Profiles of Two Genes

When to use: Check whether two co-regulated genes share the same upstream TF binding landscape.

pythonCopy code
import requests, time

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def get_gene_tfs(gene, assembly="hg38"):
    try:
        r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30)
        r.raise_for_status()
        peaks = r.json()
        return set(p.get("name", "").split(":")[0] for p in peaks if p.get("name", ""))
    except Exception as e:
        print(f"Warning: {gene} → {e}")
        return set()

gene_a, gene_b = "MYC", "MYCN"
tfs_a = get_gene_tfs(gene_a)
time.sleep(0.5)
tfs_b = get_gene_tfs(gene_b)

shared = tfs_a & tfs_b
only_a = tfs_a - tfs_b
only_b = tfs_b - tfs_a

print(f"{gene_a} TFs: {len(tfs_a)}  |  {gene_b} TFs: {len(tfs_b)}")
print(f"Shared: {len(shared)}  |  {gene_a}-only: {len(only_a)}  |  {gene_b}-only: {len(only_b)}")
print(f"\nShared TFs (first 15): {sorted(shared)[:15]}")
print(f"\n{gene_a}-only (first 10): {sorted(only_a)[:10]}")

Recipe: Export Region Peaks as BED

When to use: Export ReMap query results to BED format for downstream bedtools intersection or IGV visualization.

pythonCopy code
import requests, pandas as pd

REMAP_API = "https://remap2022.univ-amu.fr/api/v1"

def export_region_as_bed(chrom, start, end, outfile, assembly="hg38"):
    """Query ReMap region and save as 6-column BED file."""
    r = requests.get(f"{REMAP_API}/peaks/overlap/", params={
        "chr": chrom, "start": start, "end": end, "assembly": assembly
    }, timeout=30)
    r.raise_for_status()
    peaks = r.json()
    rows = [{
        "chr":   p.get("chr",   p.get("chrom", "")),
        "start": p.get("start", 0),
        "end":   p.get("end",   0),
        "name":  p.get("name",  "."),
        "score": p.get("score", 0),
        "strand": p.get("strand", "."),
    } for p in peaks]
    df = pd.DataFrame(rows)
    df = df.sort_values(["chr", "start"])
    df.to_csv(outfile, sep="\t", header=False, index=False)
    print(f"Saved {len(df)} peaks to {outfile}")
    return df

export_region_as_bed("chr17", 7_670_000, 7_690_000, "TP53_locus_remap.bed")

Troubleshooting

Related Skills

• jaspar-database — TF binding motif matrices (PWMs/PFMs); use alongside ReMap peak evidence for sequence-level validation
• encode-database — ENCODE regulatory tracks including TF ChIP-seq, DNase-seq, and ATAC-seq; partially overlaps with ReMap
• homer-motif-analysis — de novo motif discovery in ChIP-seq peak sets from ReMap or MACS3
• macs3-peak-calling — call peaks from raw ChIP-seq BAM files; ReMap provides pre-called peaks from the same approach
• regulomedb-database — regulatory variant scoring that integrates TF binding evidence similar to ReMap

References

• ReMap 2022 API documentation — REST API endpoint reference and interactive explorer
• Hammal et al., Nucleic Acids Research 2022 — ReMap 2022 paper describing the 2022 release (165M peaks, 1,210 TFs)
• ReMap portal and download page — web browser, download page for BED files and cis-regulatory modules
• Chèneby et al., Nucleic Acids Research 2020 — ReMap 2020 paper describing the reprocessing pipeline and quality control methodology