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bio-atac-seq-motif-deviation

Maintainer FreedomIntelligence · Last updated April 1, 2026

Analyze transcription factor motif accessibility variability using chromVAR. Use when identifying which TF motifs show variable accessibility across samples or conditions in ATAC-seq data.

OpenClawNanoClawAnalysisReproductionbio-atac-seq-motif-deviation🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinanalyze

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-atac-seq-motif-deviation

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: chromVAR::computeDeviations(counts, motifs).
Which TF motifs show variable accessibility across my samples?" → Compute per-sample deviation scores for TF motif accessibility to identify regulators driving chromatin state differences. R: chromVAR::computeDeviations(counts, motifs).
Measure per-sample variability in transcription factor motif accessibility using chromVAR. This identifies TFs whose binding sites show differential accessibility across conditions.
peaks <- read.table('peaks.bed', col.names = c('chr', 'start', 'end')) peak_ranges <- GRanges(seqnames = peaks$chr, ranges = IRanges(peaks$start, peaks$end)).
counts <- read.table('counts.txt', header = TRUE, row.names = 1) counts_matrix <- as.matrix(counts).

Source Doc

Excerpt From SKILL.md

Basic Workflow

Goal: Run chromVAR to compute per-sample TF motif deviation scores from ATAC-seq peak counts.

Approach: Load peak counts into a SummarizedExperiment, correct for GC bias, filter low-quality peaks, match JASPAR motifs, and compute deviation z-scores.

1. Load Peak Counts

library(chromVAR)
library(SummarizedExperiment)

## min_in_peaks=0.15: Minimum fraction of reads in peaks (FRiP). 0.15 = 15%.

fragment_counts <- filterSamples(fragment_counts, min_depth = 1500, min_in_peaks = 0.15)