TF-differential-binding
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 mechani
npx skills add majiayu000/claude-skill-registry --skill 9-tf-differential-binding --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DiffBind TF Differential Binding Analysis ## Overview This skill enables comprehensive differential TF binding analysis using **DiffBind** in R. DiffBind integrates read counting, normalization, and statistical modeling to identify differentially bound peaks between conditions. To perform DiffBind differential binding analysis: - Initialize the project directory. - Refer to the **Inputs & Outputs** section to check inputs and build the output architecture. All the output file should located in `${proj_dir}` in Step 0. - **Always prompt user** if required files are missing. - Provide a sample sheet with ChIP-seq peak files and corresponding BAM files for each sample. - Construct a `DBA` object from the sample sheet. - Compute read counts over consensus peak regions. - Specify experimental conditions (e.g., treatment vs. control or cell_type_A vs. cell_type_B). - Run statistical tests to identify differentially bound regions. - Generate correlation heatmaps, PCA plots, and volcano plots; extract significant binding events. --- ## When to use this skill Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biologic
- Overview
- When to use this skill
- Inputs & Outputs
- Inputs (choose one)
- Outputs
- Decision Tree
- Step 0: Initialize Project
- Step 1: Prepare Input Data
- Step 2: Load Data and Build the DiffBind Object
- Step 3: Read Counting and Consensus Peak Generation
- Step 4: Contrast Definition
- Step 5: Differential Binding Analysis
- Step 6: Visualization and Quality Control
- Step 7: Result Extraction
DBs/ DB_results.csv # DESeq2 results (log2FC, p-values) DB_up.bed DB_down.bed plots/ # visualization outputs PCA.pdf volcano.pdf heatmap.pdf logs/ # analysis logs temp/ # other temp files
What does the TF-differential-binding skill do?
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 mechani
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill 9-tf-differential-binding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.