bio-atac-seq-footprinting
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill footprinting --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Detect transcription factor binding footprints in ATAC-seq data by applying bias correction (e.g., TOBIAS ATACorrect) and scoring (e.g., TOBIAS ScoreBigwig), then anchor footprints to motifs to call bound/unbound sites and assess differential occupancy between conditions. Supports multiple tools (TOBIAS BINDetect, rgt-hint footprinting, Wellington, scprinter) and comparesFootprints across conditions.
How it works
- Run ATACorrect to subtract Tn5 bias from cleavage counts using a BAM, a reference genome, peaks, and a blacklist; outputs per-condition corrected bigWigs.
- Compute per-base footprint scores with ScoreBigwig on the corrected signal over consensus regions.
- Use BINDetect to anchor footprints to motifs, producing per-TF bound/unbound calls and differential occupancy with p-values.
- Provide a differential interpretation: positive or negative changes indicate conditions with stronger binding, and all results depend on motif-centric calls across conditions.
- Optionally compare TOBIAS with HINT-ATAC or other tools and reconcile discrepancies.
When to use it
Use when you need to identify bound TF sites within accessible ATAC-seq regions, compare differential TF activity between conditions, or apply bias correction prior to footprinting. Suitable for vertebrate ATAC datasets and when leveraging a motif-anchored footprinting approach.
What it can touch
- Tool: TOBIAS (ATACorrect, ScoreBigwig, BINDetect)
- Inputs: BAMs, reference genome (e.g., hg38.fa), peaks (consensus.bed), blacklist (hg38-blacklist.v2.bed)
- Outputs: corrected bigWigs, footprint scores, BINDetect results
- Optional: HINT-ATAC, Wellington, scprinter for alternative footprints and scoring
Caveats
- Requires bias correction to avoid Tn5 sequence bias confounding footprints; bias models include k-mer PWM approaches and dinucleotide corrections.
- Depth thresholds influence detection (e.g., >= 50M reads for some tools); low depth reduces reliable calls.
- Differential calls rely on per-TF footprint scores across motif sites; experiment design and replication affect interpretation.
- Cross-tool concordance recommended for high confidence; single-tool calls should be treated as exploratory.
## 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; Bao Yu 2024 bioRxiv) Tn5 has a strong sequence preference (Laz
- Version Compatibility
- Algorithmic Taxonomy
- Tn5 Bias and Why Correction Matters
- Tn5 Cut Geometry: +4 / -5 Dual-Cut
- Bias Correction Alternatives
- In Silico Variant Effect at Footprinted TF Motifs
- Per-TF Footprinting Failure Modes
- CTCF -- The gold standard
- Nuclear receptors (ER, AR, GR) -- Transient binding
- Pioneer TFs (FOXA1, GATA, OCT4) -- Half-site footprint
- AP-1 family (FOS, JUN) -- Heterodimer composite footprint
- ZBTB family / BTB-zinc finger -- Dynamic / unfootprintable
- Forkhead / homeobox (FOX, HOX) -- Short footprint < 8 bp
- Decision Tree by Goal
TOBIAS ATACorrect: produces uncorrected, bias, expected, and corrected bigWigs TOBIAS ATACorrect \ Step 1: Bias correction Step 2: Per-base footprint scoring (continuous) TOBIAS ScoreBigwig \ Step 3: Motif-anchored bound/unbound calls + differential TOBIAS BINDetect \ Filter to fragments < 100 bp (NFR) -- TF binding lives here, not on nucleosomes samtools view -h sample.bam | \ awk 'substr($0,1,1)=="@" || ($9 > 0 && $9 < 100) || ($9 < 0 && $9 > -100)' | \
What does the bio-atac-seq-footprinting skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill footprinting --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.