bio-differential-splicing
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease state
npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-splicing --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: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Differential Splicing Detect splicing changes between conditions. Tool choice is a decision about **statistical model**, **annotation dependence**, and **calibration regime** under the specific experimental design — not a preference. Wrong tool for the design produces uncalibrated FDR or systematic effect-size bias. ## Statistical Model Taxonomy | Tool | Model | Test statistic | Min reps per group | Calibration regime | Fails when | |------|-------|-----------------|---------------------|---------------------|------------| | rMATS-turbo | Binomial counts
- Version Compatibility
- Statistical Model Taxonomy
- Decision Tree by Experimental Design
- rMATS-turbo Differential Analysis
- leafcutter Differential Intron Usage
- MAJIQ V3 Differential Analysis
- SUPPA2 Differential Analysis
- Shiba for Low-Coverage / Few-Replicate Designs
- Per-Tool Failure Modes
- rMATS: Confounder-Blind LRT
- leafcutter: Cluster Mis-Topology
- MAJIQ HET: Power vs Type-1 Tradeoff
- SUPPA2 Empirical: Sparse Null at Low Replicate
- Reconciliation: When Tools Disagree
rmats.py \
for bam in *.bam; do
regtools junctions extract -a 8 -m 50 -s XS "$bam" -o "${bam%.bam}.junc"
done
ls *.junc > juncfiles.txt
python leafcutter_cluster_regtools.py \
majiq build annotation.gff3 -c settings.ini -j 8 -o build_output
majiq deltapsi \
majiq heterogen \
voila view -p 5000 -j 8 build_output/splicegraph.zarr deltapsi_output/control_treatment.deltapsi.voila -o voila_htmlWhat does the bio-differential-splicing skill do?
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease state
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-splicing --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.