footprinting

Name: footprinting
Author: GPTomics

1.1kstars185forks

How to install footprinting?

gptomics/bioskills/footprinting

$npx -y skills add GPTomics/bioSkills --skill footprinting

Installs into the current project.

Summary

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 foot

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SKILL.md

1	## Version Compatibility
2
3	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+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
10
11	# TF Footprinting
12
13	"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.
14
15	- CLI: `TOBIAS ATACorrect` -> `TOBIAS ScoreBigwig` (formerly `FootprintScores`) -> `TOBIAS BINDetect`
16	- CLI: `rgt-hint footprinting --atac-seq` (HINT-ATAC, single-step)
17	- CLI: `wellington_footprints.py` (legacy DNase, adapted for ATAC)
18	- Python: `scprinter` (multi-scale, single-cell aware; Hu 2025 Nature)
19
20	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.
21
22	## Algorithmic Taxonomy
23
24	\| Tool \| Bias model \| Scoring \| Min depth \| Strength \| Fails when \|
25	\|------\|-----------\|---------\|-----------\|----------\|------------\|
26	\| 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 \|
27	\| 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 \|
28	\| 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 \|
29	\| 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 \|
30	\| 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 \|
31	\| 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 \|
32
33	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.
34
35	## Tn5 Bias and Why Correction Matters
36
37	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.
38
39	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.
40
41	Symptom: Aggregate footprint at random GC-rich motifs shows V-shape; aggregate at AT-rich motifs shows inverse-V (peak instead of dip).
42
43	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.
44
45	Goal: Subtract Tn5 sequence-bias from the per-base cleavage signal so residual footprints reflect TF binding rather than enzyme preference.
46
47	Approach: Run TOBIAS ATACorrect over the deduplicated BAM with the reference g

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footprinting

1.1kstars185forks

View on GitHub

How to install footprinting?

gptomics/bioskills/footprinting

$npx -y skills add GPTomics/bioSkills --skill footprinting

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	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+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
8
9	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
10
11	# TF Footprinting
12
13	"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.
14
15	- CLI: `TOBIAS ATACorrect` -> `TOBIAS ScoreBigwig` (formerly `FootprintScores`) -> `TOBIAS BINDetect`
16	- CLI: `rgt-hint footprinting --atac-seq` (HINT-ATAC, single-step)
17	- CLI: `wellington_footprints.py` (legacy DNase, adapted for ATAC)
18	- Python: `scprinter` (multi-scale, single-cell aware; Hu 2025 Nature)
19
20	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.
21
22	## Algorithmic Taxonomy
23
24	\| Tool \| Bias model \| Scoring \| Min depth \| Strength \| Fails when \|
25	\|------\|-----------\|---------\|-----------\|----------\|------------\|
26	\| 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 \|
27	\| 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 \|
28	\| 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 \|
29	\| 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 \|
30	\| 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 \|
31	\| 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 \|
32
33	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.
34
35	## Tn5 Bias and Why Correction Matters
36
37	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.
38
39	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.
40
41	Symptom: Aggregate footprint at random GC-rich motifs shows V-shape; aggregate at AT-rich motifs shows inverse-V (peak instead of dip).
42
43	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.
44
45	Goal: Subtract Tn5 sequence-bias from the per-base cleavage signal so residual footprints reflect TF binding rather than enzyme preference.
46
47	Approach: Run TOBIAS ATACorrect over the deduplicated BAM with the reference g

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