数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-atac-seq-motif-deviation

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-atac-seq-motif-deviation🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinanalyze

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：chromVAR：：computeDeviations(counts，motifs)。
Which TF motifs show variable accessibility across my samples?" → Compute per-sample deviation scores ，用于 TF motif accessibility to identify regulators driving chromatin state differences. R：chromVAR：：computeDeviations(counts，motifs)。
Measure per-sample variability in transcription factor motif accessibility ，使用 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)。

原始文档

SKILL.md 摘录

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)