Footprinting

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/atac-seq/footprintingThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before…

---
name: bio-atac-seq-footprinting
description: Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before footprinting, choosing between cleavage-based and aggregate-based footprinters, or comparing differential TF activity between conditions.
tool_type: cli
primary_tool: tobias
---

## Version Compatibility

Reference examples tested with: TOBIAS 0.16+, RGT HINT-ATAC 1.0.2+, Wellington (pyDNase) 0.3+, scprinter 0.1+, samtools 1.19+, bedtools 2.31+, pyBigWig 0.3+, MEME suite 5.5+.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws unexpected errors, introspect the installed package and adapt rather than retrying.

# TF Footprinting

**"Identify TF binding footprints in my ATAC-seq data"** -> Detect short DNA stretches (typically 6-20 bp) of reduced Tn5 cleavage within accessible regions, where a bound TF physically protects DNA. Requires (1) Tn5 sequence-bias correction, (2) per-base footprint scoring, (3) motif-anchored detection.

- CLI: `TOBIAS ATACorrect` -> `TOBIAS ScoreBigwig` (formerly `FootprintScores`) -> `TOBIAS BINDetect`
- CLI: `rgt-hint footprinting --atac-seq` (HINT-ATAC, single-step)
- CLI: `wellington_footprints.py` (legacy DNase, adapted for ATAC)
- Python: `scprinter` (multi-scale, single-cell aware; Hu 2025 Nature)

Tn5 has a strong sequence preference (Karabacak Calviello 2019), reading approximately +/- 4 bp around the insertion site. Without bias correction, "footprints" reflect Tn5 sequence preference rather than TF binding. This is the single most important step.

## Algorithmic Taxonomy

| Tool | Bias model | Scoring | Min depth | Strength | Fails when |
|------|-----------|---------|-----------|----------|------------|
| TOBIAS (BINDetect) | +/-12 bp k-mer window (`--k_flank` 12), dinucleotide weight matrix (DWM) | Two-step: continuous footprint score then motif-anchored bound/unbound classification | >= 50M nuclear reads | Mature, peer-reviewed (Bentsen 2020), differential support, modular pipeline | Below 50M reads; sequencing errors near motif inflate background |
| HINT-ATAC | Hidden-Markov + dinucleotide bias correction | HMM emits open/footprint/closed states; calls ranked footprints | >= 50M | Single-step; integrates motif matching; handles DNase too | Less control over individual stages; HMM occasionally over-segments |
| Wellington (pyDNase) | DNase-developed; ATAC adaptation by post-shift | Cleavage-rate Poisson Z-score | >= 50M (DNase >= 80M) | Original footprinting framework; well-validated for DNase | Designed for DNase II; ATAC-specific bias not corrected as carefully |
| PIQ | Bayesian latent variable on cut sites | Genome-wide PWM scan + cleavage profile | >= 30M (lower because of model) | Per-TF posterior probabilities; works on lower depth | Outdated; not actively maintained; harder to install |
| scprinter | Multi-scale CNN-based footprint and TF activity | Resolves footprints at multiple TF size scales (CTCF vs nuclear receptors) | >= 1M cells (sc) or 50M (bulk) | Modern ML approach; single-cell; multi-scale resolves problematic TF families | Newer tool; benchmarks evolving; GPU recommended |
| TOBIAS + scprinter combination | TOBIAS bias correction + scprinter scoring | Two-step bridging | >= 50M | Combines the best bias model with multi-scale scoring | Manual pipeline, no single CLI |

Methodology evolves; verify against the current Bentsen 2020, Karabacak Calviello 2019, and scPrinter (Hu 2025) benchmarks. ATAC footprinting power saturates above 100M nuclear reads; below 50M, weak-binding TFs (transient occupancy) cannot be reliably called.

## Tn5 Bias and Why Correction Matters

**Trigger:** Tn5 inserts preferentially at certain k-mers (Karabacak Calviello 2019 measured the protocol-specific 6-mer insertion-bias model used for bias correction). The preference is reproducible and biologically uninteresting.

**Mechanism:** Without correction, every "TF footprint" near a high-bias k-mer reads as occupancy. Conversely, regions with low-bias flanks but real binding may show no footprint dip relative to corrected expectation.

**Symptom:** Aggregate footprint at random GC-rich motifs shows V-shape; aggregate at AT-rich motifs shows inverse-V (peak instead of dip).

**Fix:** Apply ATACorrect (TOBIAS), seqOutBias (Martins 2018), or HINT's dinucleotide model. Bias correction subtracts the Tn5-expected per-base profile from observed cleavage. After correction, V-shape is preserved only at TF-bound sites.

**Goal:** Subtract Tn5 sequence-bias from the per-base cleavage signal so residual footprints reflect TF binding rather than enzyme preference.

**Approach:** Run TOBIAS ATACorrect over the deduplicated BAM with the reference genome, consensus peaks, and ENCODE blacklist; it emits per-condition uncorrected, bias, expected, and corrected bigWigs for downstream scoring.

```bash
# TOBIAS ATACorrect: produces uncorrected, bias, expected, and corrected bigWigs
TOBIAS ATACorrect \
    --bam sample.dedup.bam \
    --genome hg38.fa \
    --peaks consensus_peaks.bed \
    --blacklist hg38-blacklist.v2.bed \
    --outdir corrected/ \
    --cores 8
# Output: sample_uncorrected.bw, sample_bias.bw, sample_expected.bw, sample_corrected.bw
```

## Tn5 Cut Geometry: +4 / -5 Dual-Cut

Tn5 dimers cut both strands of DNA but with a 9 bp staggered offset. The cleavage event creates two free 5' ends: one shifted +4 bp from the binding center on the forward strand and -5 bp on the reverse strand. Footprinting tools must apply this shift before per-base counting:

| Strand | Read 5' end correction |
|--------|------------------------|
| + strand | shift +4 bp downstream |
| - strand | shift -5 bp upstream |

**Trigger:** Computin

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pyBigWig    # scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter && cd scPrinter && p

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Footprinting

• github.com/GPTomics/bioSkills/tree/main/atac-seq/footprinting — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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