bio-clip-seq-differential-clip
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-clip --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.
## Version Compatibility Reference examples tested with: DEWSeq 1.18+, htseq-clip 2.0+, DESeq2 1.44+, edgeR 4.2+, limma 3.60+, Flipper (commit 2024.04+), Skipper (commit 2023.05+), pybedtools 0.10+, pyranges 0.0.129+. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying. # Differential CLIP-seq Analysis **"Identify regions with changed RBP binding across conditions"** -> Test for condition-level differences in IP enrichment relative to SMInput, accounting for replicate variance and (where available) sequencing depth normalization. Three statistical scales are possible: peak-level (test each peak as a unit), window-level (test fixed transcriptome windows; DEWSeq, Flipper), or crosslink-site level (test single-nt positions). The choice depends on the biology (narrow regulatory shift vs broad binding-mode change
- Version Compatibility
- Algorithmic Taxonomy
- Critical Decision: The Interaction-Term Design
- Critical Choice: Peak-Level vs Window-Level vs Crosslink-Level
- DEWSeq Workflow (Window-Level Differential)
- Flipper Workflow (Skipper-Coupled)
- Peak-Level Differential (CLIPper Upstream)
- RBP Knockdown Experiment Design
- Per-Tool Failure Modes
- DEWSeq -- Slow on dense libraries
- DEWSeq -- Custom adjacency aggregation needed
- Peak-level differential -- Peak set differs between conditions
- Interaction term forgotten
- Normalization assumptions
Step 1: htseq-clip generates sliding-window count matrices htseq-clip extract \ Step 2: count IP and SMInput reads per window per sample for sample in ip_rep1 ip_rep2 sminput_rep1 sminput_rep2; do htseq-clip count \ done Step 3: DEWSeq differential testing htseq-clip mergeCounts \ Assume Skipper has been run on all samples; Skipper output is at skipper_out/ flipper differential \
What does the bio-clip-seq-differential-clip skill do?
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-clip --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.