28.ATACseq-footprint/SKILL.md
This skill performs transcription factor (TF) footprint analysis using TOBIAS on ATAC-seq data. It corrects Tn5 sequence bias, quantifies TF occupancy at motif sites, generates footprint scores, and optionally compares differential TF binding across conditions.
npx skillsauth add bisnake2001/chromskills atac-footprintingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill performs TF footprint detection and optional differential TF binding analysis using TOBIAS. It identifies true TF occupancy by modeling depletion of Tn5 insertions at motif cores.
Main steps include:
Use this skill when you need to identify transcription factor occupancy using ATAC-seq data. It is suitable for:
Recommended data requirements:
Required:
Optional:
ATAC_footprint_analysis/
01_ATACorrect/
<sample>_corrected.bw # bias-corrected insertion signal
<sample>_bias_model.txt # Tn5 bias report
<sample>_correction.log # logs
02_TFBScan/
motifs.bed # motif genomic coordinates
scan.log
03_ScoreBigwig/
<sample>_ftprints.bw # footprint score bigWig
score.log
04_BINDetect/
<TF>_sites.bed # bound/unbound calls per TF
motif_activity.txt # differential TF activity (if applicable)
bindetect.log
05_PlotAggregate/
<TF>_aggregate.pdf # aggregate footprint plots
aggregate.log
logs/ # all logs
temp/ # intermediate files
User must confirm missing files.
Call:
with:
bam: Path to input ATAC-seq BAM filepeaks: Path to peak BED / narrowPeak file restricting insertion sitesgenome: HOMER genome identifier, e.g. 'hg38', 'mm10'outdir: Output directory for ATACorrect results (e.g. 01_ATACorrect)prefix: Prefix / sample name used for output filesCall:
with:
motifs: Path to motif PWM file or directory (e.g. JASPAR *.jaspar)genome: HOMER genome identifier, e.g. 'hg38', 'mm10'regions_bed: BED file defining regions to scan (typically ATAC peaks)outdir: Output directory for TFBScan results (e.g. 02_TFBScan)prefix: Prefix / sample name used for output filesCall:
with:
signal_bw: Bias-corrected signal bigWig (e.g. from Step 2)regions_bed: Motif regions BED file (e.g. from Step 3)output_bw: Output bigWig path for footprint scorescores: Number of CPU cores to useCall:
with:
signals: List of footprint bigWig filespeaks_bed: BED file of peaks (or merged peaks for multi-condition analysis)motifs: Path to motif PWM file or directory (e.g. JASPAR *.jaspar)genome: HOMER genome identifier, e.g. 'hg38', 'mm10'outdir: Output directory for BINDetect results (e.g. 04_BINDetect)conditions: Optional list of condition labels matching the order of signalsCall:
with:
signals: List of bigWig signal files to plot (e.g. corrected ATAC signal)tfbs_bed: BED file of TF binding sites (e.g. from BINDetect, <TF>_sites.bed)motifs: Path to motif PWM file or directory (e.g. JASPAR *.jaspar)flank: Number of bp flanking the motif to include in the aggregate plotoutput_pdf: Output PDF file path for the aggregate footprint plotdevelopment
Align ChIP-seq or ATAC-seq FASTQ files to a reference genome using Bowtie2, with strict input validation, library layout detection, output organization and logging. Use it when raw sequencing reads must be converted into sorted/indexed BAM files before downstream QC, peak calling, or footprinting.
development
Align bisulfite sequencing DNA methylation reads using Bismark only, with explicit validation of reference preparation, library layout detection, output organization, logging, and alignment QC. Use it for WGBS, RRBS, or other bisulfite-converted DNA methylation sequencing data when raw FASTQ files must be aligned before methylation extraction and downstream analysis.
data-ai
Perform peak calling for ChIP-seq or ATAC-seq data using MACS3, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.
devops
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.