bio-splicing-quantification
Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normaliz
npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-quantification --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+, IRFinder-S 2.0+, kallisto 0.50+, Salmon 1.10+, pandas 2.2+, STAR 2.7.11+ 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. # Splicing Quantification Quantify alternative splicing events as PSI (percent spliced in) from RNA-seq. PSI = inclusion read evidence / (inclusion + skipping read evidence), normalized for differential mapping opportunity between isoforms. The choice of *quantification unit* (event, intron cluster, LSV, transcript) determines which biological questions can be answered and which failure modes apply. ## Algorithmic Taxonomy | Family | Unit | Reference tools | Fails when | |--------|------|-----------------|------------| | Event-based | Pre-defin
- Version Compatibility
- Algorithmic Taxonomy
- Event Taxonomy (Beyond Standard SE/A5SS/A3SS/MXE/RI)
- Tool Selection Matrix
- PSI Definition and Effective Length Normalization
- Sign Conventions for Alternative Splice Sites
- rMATS-turbo Workflow
- JC vs JCEC files
- SUPPA2 Workflow
- MAJIQ V3 Workflow
- leafcutter Junction Quantification
- Per-Tool Failure Modes
- rMATS-turbo: Junction Read Imbalance
- SUPPA2: Sparse Empirical Null at Low Replicate Count
rmats.py \
suppa.py generateEvents -i annotation.gtf -o events -f ioe -e SE SS MX RI AF AL
for ev in SE A5 A3 MX RI; do
suppa.py psiPerEvent -i events_${ev}_strict.ioe -e transcript_tpm.tsv -o psi_${ev}
done
majiq build annotation.gff3 -c settings.ini -j 8 -o build_output
majiq psi build_output/sample1.majiq build_output/sample2.majiq -j 4 -o psi_output -n condition_psi
voila view -p 5000 -j 8 build_output/splicegraph.zarr psi_output/condition_psi.psi.voila -o voila_output
for bam in *.bam; do
regtools junctions extract -a 8 -m 50 -s XS "$bam" -o "${bam%.bam}.junc"What does the bio-splicing-quantification skill do?
Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normaliz
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-quantification --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.