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-differential-accessibility

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

Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.

OpenClawNanoClaw分析处理复现实验bio-atac-seq-differential-accessibility🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinfind

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

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：DiffBind 或 DESeq2 on peak-by-sample count matrix。
Find differentially accessible regions between my conditions" → Identify chromatin regions ，支持 statistically significant changes in accessibility between treatment groups，cell types，或 timepoints. R：DiffBind 或 DESeq2 on peak-by-sample count matrix。
samples <- data.frame( SampleID = c('ctrl_1'，'ctrl_2'，'treat_1'，'treat_2')，Condition = c('control'，'control'，'treated'，'treated')，Replicate = c(1，2，1，2)，bamReads = c('ctrl_1.bam'，'ctrl_2.bam'，'treat_1.bam'，'treat_2.bam')，Peaks = c('ctrl_1.narrowPeak'，'ctrl_2.narrowPeak'，'treat_1.narrowPeak'，'treat_2.narrowPeak') ) write.csv(samples，'samples.csv'，row.names=FALSE)。

原始文档

SKILL.md 摘录

DiffBind Workflow

Goal: Identify differentially accessible chromatin regions between experimental conditions.

Approach: Load sample metadata and peak files into DiffBind, count reads in consensus peaks, normalize, define contrasts, and run differential analysis with DESeq2 backend.

library(DiffBind)

## 2. Load data

dba <- dba(sampleSheet='samples.csv')

## 5. Set up contrasts

dba <- dba.contrast(dba, contrast=c('Condition', 'treated', 'control'))